Package 'htetree' reference manual

Title:	Causal Inference with Tree-Based Machine Learning Algorithms
Description:	Estimating heterogeneous treatment effects with tree-based machine learning algorithms and visualizing estimated results in flexible and presentation-ready ways. For more information, see Brand, Xu, Koch, and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package first started as a fork of the 'causalTree' package on 'GitHub' and we greatly appreciate the authors for their extremely useful and free package.
Authors:	Jiahui Xu [cre, aut], Tanvi Shinkre [aut], Jennie Brand [aut]
Maintainer:	Jiahui Xu <[email protected]>
License:	GPL-2 \| GPL-3
Version:	0.1.19
Built:	2024-10-14 04:00:42 UTC
Source:	https://github.com/cran/htetree

Include the Javascript Used in Shiny

Description

intermediate function used to include necessary javascript to visualize tree structures and estimated treatment effect in shiny

Usage

bundScript(...)
bundScript(...)

Arguments

...

There is no required arguments in this function. But user could manipulate to include different css files.

Value

No return value. It is used to pass the Javascript to Shiny.

Causal Effect Regression and Estimation Trees

Description

Fit a causalTree model to get an rpart object

Usage

causalTree(
  formula,
  data,
  weights,
  treatment,
  subset,
  na.action = na.causalTree,
  split.Rule,
  split.Honest,
  HonestSampleSize,
  split.Bucket,
  bucketNum = 5,
  bucketMax = 100,
  cv.option,
  cv.Honest,
  minsize = 2L,
  x = FALSE,
  y = TRUE,
  propensity,
  control,
  split.alpha = 0.5,
  cv.alpha = 0.5,
  cv.gamma = 0.5,
  split.gamma = 0.5,
  cost,
  ...
)
causalTree(
  formula,
  data,
  weights,
  treatment,
  subset,
  na.action = na.causalTree,
  split.Rule,
  split.Honest,
  HonestSampleSize,
  split.Bucket,
  bucketNum = 5,
  bucketMax = 100,
  cv.option,
  cv.Honest,
  minsize = 2L,
  x = FALSE,
  y = TRUE,
  propensity,
  control,
  split.alpha = 0.5,
  cv.alpha = 0.5,
  cv.gamma = 0.5,
  split.gamma = 0.5,
  cost,
  ...
)

Arguments

`formula`	a formula, with a response and features but no interaction terms. If this a a data frome, that is taken as the model frame (see `model.frame).`
`data`	an optional data frame that includes the variables named in the formula.
`weights`	optional case weights.
`treatment`	a vector that indicates the treatment status of each observation. 1 represents treated and 0 represents control. Only binary treatment supported in this version.
`subset`	optional expression saying that only a subset of the rows of the data should be used in the fit.
`na.action`	the default action deletes all observations for which `y` is missing, but keeps those in which one or more predictors are missing.
`split.Rule`	causalTree splitting options, one of `"TOT"`, `"CT"`, `"fit"`, `"tstats"`, four splitting rules in `causalTree`. Note that the `"tstats"` alternative does not have an associated cross-validation method `cv.option`; see Athey and Imbens (2016) for a discussion. Note further that `split.Rule` and `cv.option` can mix and match.
`split.Honest`	boolean option, `TRUE` or `FALSE`, used for `split.Rule` as `"CT"` or `"fit"`. If set as `TRUE`, do honest splitting, with default `split.alpha` = 0.5; if set as `FALSE`, do adaptive splitting with `split.alpha` = 1. The user choice of `split.alpha` will be ignored if `split.Honest` is set as `FALSE`, but will be respected if set to `TRUE`. For `split.Rule`=`"TOT"`, there is no honest splitting option and the parameter `split.alpha` does not matter. For `split.Rule`=`"tstats"`, a value of `TRUE` enables use of `split.alpha` in calculating the risk function, which determines the order of pruning in cross-validation. Note also that causalTree function returns the estimates from the training data, no matter what the value of `split.Honest` is; the tree must be re-estimated to get the honest estimates using `estimate.causalTree`. The wrapper function `honest.CausalTree` does honest estimation in one step and returns a tree.
`HonestSampleSize`	number of observations anticipated to be used in honest re-estimation after building the tree. This enters the risk function used in both splitting and cross-validation.
`split.Bucket`	boolean option, `TRUE` or `FALSE`, used to specify whether to apply the discrete method in splitting the tree. If set as `TRUE`, in splitting a node, the observations in a leaf will be be partitioned into buckets, with each bucket containing `bucketNum` treated and `bucketNum` control units, and where observations are ordered prior to partitioning. Splitting will take place by bucket.
`bucketNum`	number of observations in each bucket when set `split.Bucket` = `TRUE`. However, the code will override this choice in order to guarantee that there are at least `minsize` and at most `bucketMax` buckets.
`bucketMax`	Option to choose maximum number of buckets to use in splitting when set `split.Bucket` = `TRUE`, `bucketNum` can change by choice of `bucketMax`.
`cv.option`	cross validation options, one of `"TOT"`, `"matching"`, `"CT"`, `"fit"`, four cross validation methods in causalTree. There is no `cv.option` for the `split.Rule` `"tstats"`; see Athey and Imbens (2016) for discussion.
`cv.Honest`	boolean option, `TRUE` or `FALSE`, only used for `cv.option` as `"CT"` or `"fit"`, to specify whether to apply honest risk evalation function in cross validation. If set `TRUE`, use honest risk function, otherwise use adaptive risk function in cross validation. If set `FALSE`, the user choice of `cv.alpha` will be set to 1. If set `TRUE`, `cv.alpha` will default to 0.5, but the user choice of `cv.alpha` will be respected. Note that honest cv estimates within-leaf variances and may perform better with larger leaf sizes and/or small number of cross-validation sets.
`minsize`	in order to split, each leaf must have at least `minsize` treated cases and `minsize` control cases. The default value is set as 2.
`x`	keep a copy of the `x` matrix in the result.
`y`	keep a copy of the dependent variable in the result. If missing and `model` is supplied this defaults to `FALSE`.
`propensity`	propensity score used in `"TOT"` splitting and `"TOT"`, honest `"CT"` cross validation methods. The default value is the proportion of treated cases in all observations. In this implementation, the propensity score is a constant for the whole dataset. Unit-specific propensity scores are not supported; however, the user may use inverse propensity scores as case weights if desired.
`control`	a list of options that control details of the `rpart` algorithm. See `rpart.control`.
`split.alpha`	scale parameter between 0 and 1, used in splitting risk evaluation function for `"CT"`. When `split.Honest = FALSE`, `split.alpha` will be set as 1. For `split.Rule`=`"tstats"`, if `split.Honest`=`TRUE`, `split.alpha` is used in calculating the risk function, which determines the order of pruning in cross-validation.
`cv.alpha`	scale paramter between 0 and 1, used in cross validation risk evaluation function for `"CT"` and `"fit"`. When `cv.Honest = FALSE`, `cv.alpha` will be set as 1.
`cv.gamma`, `split.gamma`	optional parameters used in evaluating policies.
`cost`	a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.
`...`	arguments to `rpart.control` may also be specified in the call to `causalTree`. They are checked against the list of valid arguments. An example of a commonly set parameter would be `xval`, which sets the number of cross-validation samples. The parameter `minsize` is implemented differently in `causalTree` than in `rpart`; we require a minimum of `minsize` treated observations and a minimum of `minsize` control observations in each leaf.

Details

CausalTree differs from rpart function from rpart package in splitting rules and cross validation methods. Please check Athey and Imbens, Recursive Partitioning for Heterogeneous Causal Effects (2016) for more details.

Value

An object of class rpart. See rpart.object.

References

Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.

Athey, S and G Imbens (2016) Recursive Partitioning for Heterogeneous Causal Effects. http://arxiv.org/abs/1504.01132

Examples

library("htetree")
library("rpart")
library("rpart.plot")
tree <- causalTree(y~ x1 + x2 + x3 + x4, data = simulation.1,
treatment = simulation.1$treatment,
split.Rule = "CT", cv.option = "CT", split.Honest = TRUE, cv.Honest = TRUE,
split.Bucket = FALSE, xval = 5,
cp = 0, minsize = 20, propensity = 0.5)

opcp <- tree$cptable[,1][which.min(tree$cptable[,4])]

opfit <- prune(tree, opcp)

rpart.plot(opfit)

fittree <- causalTree(y~ x1 + x2 + x3 + x4, data = simulation.1,
                      treatment = simulation.1$treatment,
                      split.Rule = "fit", cv.option = "fit",
                      split.Honest = TRUE, cv.Honest = TRUE, split.Bucket = TRUE,
                      bucketNum = 5,
                      bucketMax = 200, xval = 10,
                      cp = 0, minsize = 20, propensity = 0.5)

tstatstree <- causalTree(y~ x1 + x2 + x3 + x4, data = simulation.1,
                         treatment = simulation.1$treatment,
                         split.Rule = "tstats", cv.option = "CT",
                         cv.Honest = TRUE, split.Bucket = TRUE,
                         bucketNum = 10,
                         bucketMax = 200, xval = 5,
                         cp = 0, minsize = 20, propensity = 0.5)
library("htetree")
library("rpart")
library("rpart.plot")
tree <- causalTree(y~ x1 + x2 + x3 + x4, data = simulation.1,
treatment = simulation.1$treatment,
split.Rule = "CT", cv.option = "CT", split.Honest = TRUE, cv.Honest = TRUE,
split.Bucket = FALSE, xval = 5,
cp = 0, minsize = 20, propensity = 0.5)

opcp <- tree$cptable[,1][which.min(tree$cptable[,4])]

opfit <- prune(tree, opcp)

rpart.plot(opfit)

fittree <- causalTree(y~ x1 + x2 + x3 + x4, data = simulation.1,
                      treatment = simulation.1$treatment,
                      split.Rule = "fit", cv.option = "fit",
                      split.Honest = TRUE, cv.Honest = TRUE, split.Bucket = TRUE,
                      bucketNum = 5,
                      bucketMax = 200, xval = 10,
                      cp = 0, minsize = 20, propensity = 0.5)

tstatstree <- causalTree(y~ x1 + x2 + x3 + x4, data = simulation.1,
                         treatment = simulation.1$treatment,
                         split.Rule = "tstats", cv.option = "CT",
                         cv.Honest = TRUE, split.Bucket = TRUE,
                         bucketNum = 10,
                         bucketMax = 200, xval = 5,
                         cp = 0, minsize = 20, propensity = 0.5)

Compute the "branches" to be drawn for an `causalTree` object

Description

Compute the "branches" to be drawn for an causalTree object

Usage

causalTree.branch(x, y, node, branch)
causalTree.branch(x, y, node, branch)

Arguments

`x`	covariates
`y`	outcome
`node`	node of the fitted tree
`branch`	branch of the fitted tree

Value

number of branches to be drawn

Intermediate function for `causalTree`

Description

Intermediate function for causalTree

Usage

causalTree.control(
  minsplit = 20L,
  minbucket = round(minsplit/3),
  cp = 0,
  maxcompete = 4L,
  maxsurrogate = 5L,
  usesurrogate = 2L,
  xval = 10L,
  surrogatestyle = 0L,
  maxdepth = 30L,
  ...
)
causalTree.control(
  minsplit = 20L,
  minbucket = round(minsplit/3),
  cp = 0,
  maxcompete = 4L,
  maxsurrogate = 5L,
  usesurrogate = 2L,
  xval = 10L,
  surrogatestyle = 0L,
  maxdepth = 30L,
  ...
)

Arguments

`minsplit`	minimum number of splits
`minbucket`	minimum number of bucket
`cp`	default is 0
`maxcompete`	maximum number of compete
`maxsurrogate`	maximum number of surrogate
`usesurrogate`	initial number of surrogate
`xval`	cross-validation
`surrogatestyle`	the style of surrogate
`maxdepth`	Maximum depth
`...`	arguments to `rpart.control` may also be specified in the call to `causalTree`. They are checked against the list of valid arguments. An example of a commonly set parameter would be `xval`, which sets the number of cross-validation samples. The parameter `minsize` is implemented differently in `causalTree` than in `rpart`; we require a minimum of `minsize` treated observations and a minimum of `minsize` control observations in each leaf.

Value

parameters used to in causalTree

Intermediate function for `causalTree`

Description

Intermediate function for causalTree

Usage

causalTree.matrix(frame)
causalTree.matrix(frame)

Arguments

frame

inherited from data.frame

Value

A covariate matrix used in the causal regression.

Intermediate function for `causalTree`

Description

This routine sets up the callback code for user-written split routines in causalTree

Usage

causalTreecallback(mlist, nobs, init)
causalTreecallback(mlist, nobs, init)

Arguments

`mlist`	a list of user written methods
`nobs`	number of observations
`init`	function name

Value

split method written by users

Intermediate function for `causalTree`

Description

Compute the x-y coordinates for a tree

Usage

causalTreeco(tree, parms)
causalTreeco(tree, parms)

Arguments

`tree`	an `causalTree` object
`parms`	parms

Value

the x-y coordinates for a tree

Clear Temporary Files

Description

The files for shiny are saved in a temporary directory. The files can be cleared manually using the 'clearTemp()' function, or will automatically be cleared when you close R

Usage

clearTemp()
clearTemp()

Value

no return value, to unlink files under the temp folder

Intermediate function for `causalTree`

Description

Run down the built tree and get the final leaf ids for estimation sample

Usage

est.causalTree(fit, x)
est.causalTree(fit, x)

Arguments

`fit`	an `causalTree` object
`x`	covariates

Value

Intermediate estimation results for an causalTree object.

estimate causal Tree

Description

estimate causal Tree

Usage

estimate.causalTree(
  object,
  data,
  weights,
  treatment,
  na.action = na.causalTree
)
estimate.causalTree(
  object,
  data,
  weights,
  treatment,
  na.action = na.causalTree
)

Arguments

`object`	A tree-structured fit `rpart` object, such as one generated as a `causalTree` fit.
`data`	New data frame to be used for estimating effects within leaves.
`weights`	optional case weights.
`treatment`	The treatment status of observations in the new dataframe, where 1 represents treated and 0 represents control.
`na.action`	the default action deletes all observations for which `y` is missing, but keeps those in which one or more predictors are missing.

Details

When the leaf contains only treated or control cases, the function will trace back to the leaf's parent node recursively until the parent can be used to compute causal effect. Please see Athey and Imbens Machine Learning Methods for Estimating Heterogeneous Causal Effects (2015) for details.

Value

Intermediate estimation results for an causalTree object

Intermediate function for `causalTree`

Description

Intermediate function for causalTree

Usage

formatg(x, digits = getOption("digits"), format = paste0("%.", digits, "g"))
formatg(x, digits = getOption("digits"), format = paste0("%.", digits, "g"))

Arguments

`x`	input training data
`digits`	number of digits to be kept
`format`	format of exported vector

Value

No return value, called for formatting the exported estimates

Get the Current Working Directory

Description

get the current work directory and set it as the default directory to save the shiny files temporarily

Usage

getDefaultPath()
getDefaultPath()

Value

a temporary file path

Getting Distribution in Treatment and Control Groups

Description

Getting the density of distribution in treatment and control groups, which will be displayed in the

Usage

getDensities(treatment, outcome)
getDensities(treatment, outcome)

Arguments

`treatment`	A character representing the name of treatment indicator.
`outcome`	A character representing the name of outcome variable.

Value

vector of corresponding densities for each value of outcome vector

Causal Effect Regression and Estimation Trees: One-step honest estimation

Description

Fit a causalTree model to get an honest causal tree, with tree structure built on training sample (including cross-validation) and leaf estimates taken from estimation sample. Return an rpart object.

Usage

honest.causalTree(
  formula,
  data,
  weights,
  treatment,
  subset,
  est_data,
  est_weights,
  est_treatment,
  est_subset,
  na.action = na.causalTree,
  split.Rule,
  split.Honest,
  HonestSampleSize,
  split.Bucket,
  bucketNum = 10,
  bucketMax = 40,
  cv.option,
  cv.Honest,
  minsize = 2L,
  model = FALSE,
  x = FALSE,
  y = TRUE,
  propensity,
  control,
  split.alpha = 0.5,
  cv.alpha = 0.5,
  cv.gamma = 0.5,
  split.gamma = 0.5,
  cost,
  ...
)
honest.causalTree(
  formula,
  data,
  weights,
  treatment,
  subset,
  est_data,
  est_weights,
  est_treatment,
  est_subset,
  na.action = na.causalTree,
  split.Rule,
  split.Honest,
  HonestSampleSize,
  split.Bucket,
  bucketNum = 10,
  bucketMax = 40,
  cv.option,
  cv.Honest,
  minsize = 2L,
  model = FALSE,
  x = FALSE,
  y = TRUE,
  propensity,
  control,
  split.alpha = 0.5,
  cv.alpha = 0.5,
  cv.gamma = 0.5,
  split.gamma = 0.5,
  cost,
  ...
)

Arguments

`formula`	a formula, with a response and features but no interaction terms. If this a a data frome, that is taken as the model frame (see `model.frame).`
`data`	an optional data frame that includes the variables named in the formula.
`weights`	optional case weights.
`treatment`	a vector that indicates the treatment status of each observation. 1 represents treated and 0 represents control. Only binary treatment supported in this version.
`subset`	optional expression saying that only a subset of the rows of the data should be used in the fit.
`est_data`	data frame to be used for leaf estimates; the estimation sample. Must contain the variables used in training the tree.
`est_weights`	optional case weights for estimation sample
`est_treatment`	treatment vector for estimation sample. Must be same length as estimation data. A vector indicates the treatment status of the data, 1 represents treated and 0 represents control. Only binary treatment supported in this version.
`est_subset`	optional expression saying that only a subset of the rows of the estimation data should be used in the fit of the re-estimated tree.
`na.action`	the default action deletes all observations for which `y` is missing, but keeps those in which one or more predictors are missing.
`split.Rule`	causalTree splitting options, one of `"TOT"`, `"CT"`, `"fit"`, `"tstats"`, four splitting rules in `causalTree`. Note that the `"tstats"` alternative does not have an associated cross-validation method `cv.option`; see Athey and Imbens (2016) for a discussion. Note further that `split.Rule` and `cv.option` can mix and match.
`split.Honest`	boolean option, `TRUE` or `FALSE`, used for `split.Rule` as `"CT"` or `"fit"`. If set as `TRUE`, do honest splitting, with default `split.alpha` = 0.5; if set as `FALSE`, do adaptive splitting with `split.alpha` = 1. The user choice of `split.alpha` will be ignored if `split.Honest` is set as `FALSE`, but will be respected if set to `TRUE`. For `split.Rule`=`"TOT"`, there is no honest splitting option and the parameter `split.alpha` does not matter. For `split.Rule`=`"tstats"`, a value of `TRUE` enables use of `split.alpha` in calculating the risk function, which determines the order of pruning in cross-validation. Note also that causalTree function returns the estimates from the training data, no matter what the value of `split.Honest` is; the tree must be re-estimated to get the honest estimates using `estimate.causalTree`. The wrapper function `honest.CausalTree` does honest estimation in one step and returns a tree.
`HonestSampleSize`	number of observations anticipated to be used in honest re-estimation after building the tree. This enters the risk function used in both splitting and cross-validation.
`split.Bucket`	boolean option, `TRUE` or `FALSE`, used to specify whether to apply the discrete method in splitting the tree. If set as `TRUE`, in splitting a node, the observations in a leaf will be be partitioned into buckets, with each bucket containing `bucketNum` treated and `bucketNum` control units, and where observations are ordered prior to partitioning. Splitting will take place by bucket.
`bucketNum`	number of observations in each bucket when set `split.Bucket` = `TRUE`. However, the code will override this choice in order to guarantee that there are at least `minsize` and at most `bucketMax` buckets.
`bucketMax`	Option to choose maximum number of buckets to use in splitting when set `split.Bucket` = `TRUE`, `bucketNum` can change by choice of `bucketMax`.
`cv.option`	cross validation options, one of `"TOT"`, `"matching"`, `"CT"`, `"fit"`, four cross validation methods in causalTree. There is no `cv.option` for the `split.Rule` `"tstats"`; see Athey and Imbens (2016) for discussion.
`cv.Honest`	boolean option, `TRUE` or `FALSE`, only used for `cv.option` as `"CT"` or `"fit"`, to specify whether to apply honest risk evalation function in cross validation. If set `TRUE`, use honest risk function, otherwise use adaptive risk function in cross validation. If set `FALSE`, the user choice of `cv.alpha` will be set to 1. If set `TRUE`, `cv.alpha` will default to 0.5, but the user choice of `cv.alpha` will be respected. Note that honest cv estimates within-leaf variances and may perform better with larger leaf sizes and/or small number of cross-validation sets.
`minsize`	in order to split, each leaf must have at least `minsize` treated cases and `minsize` control cases. The default value is set as 2.
`model`	model frame of `causalTree`, same as `rpart`
`x`	keep a copy of the `x` matrix in the result.
`y`	keep a copy of the dependent variable in the result. If missing and `model` is supplied this defaults to `FALSE`.
`propensity`	propensity score used in `"TOT"` splitting and `"TOT"`, honest `"CT"` cross validation methods. The default value is the proportion of treated cases in all observations. In this implementation, the propensity score is a constant for the whole dataset. Unit-specific propensity scores are not supported; however, the user may use inverse propensity scores as case weights if desired.
`control`	a list of options that control details of the `rpart` algorithm. See `rpart.control`.
`split.alpha`	scale parameter between 0 and 1, used in splitting risk evaluation function for `"CT"`. When `split.Honest = FALSE`, `split.alpha` will be set as 1. For `split.Rule`=`"tstats"`, if `split.Honest`=`TRUE`, `split.alpha` is used in calculating the risk function, which determines the order of pruning in cross-validation.
`cv.alpha`	scale paramter between 0 and 1, used in cross validation risk evaluation function for `"CT"` and `"fit"`. When `cv.Honest = FALSE`, `cv.alpha` will be set as 1.
`cv.gamma`, `split.gamma`	optional parameters used in evaluating policies.
`cost`	a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.
`...`	arguments to `rpart.control` may also be specified in the call to `causalTree`. They are checked against the list of valid arguments. An example of a commonly set parameter would be `xval`, which sets the number of cross-validation samples. The parameter `minsize` is implemented differently in `causalTree` than in `rpart`; we require a minimum of `minsize` treated observations and a minimum of `minsize` control observations in each leaf.

Value

An object of class rpart. See rpart.object.

References

Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.

Athey, S and G Imbens (2016) Recursive Partitioning for Heterogeneous Causal Effects. http://arxiv.org/abs/1504.01132

Examples

library("rpart")
library("rpart.plot")
library("htetree")
n <- nrow(simulation.1)

trIdx <- which(simulation.1$treatment == 1)

conIdx <- which(simulation.1$treatment == 0)

train_idx <- c(sample(trIdx, length(trIdx) / 2), sample(conIdx,
    length(conIdx) / 2))

train_data <- simulation.1[train_idx, ]

est_data <- simulation.1[-train_idx, ]

honestTree <- honest.causalTree(y ~ x1 + x2 + x3 + x4, data = train_data,
     treatment = train_data$treatment,
     est_data = est_data,
     est_treatment = est_data$treatment,
     split.Rule = "CT", split.Honest = TRUE,
     HonestSampleSize = nrow(est_data),
     split.Bucket = TRUE, cv.option = "CT")

opcp <-  honestTree$cptable[,1][which.min(honestTree$cptable[,4])]

opTree <- prune(honestTree, opcp)

rpart.plot(opTree)
library("rpart")
library("rpart.plot")
library("htetree")
n <- nrow(simulation.1)

trIdx <- which(simulation.1$treatment == 1)

conIdx <- which(simulation.1$treatment == 0)

train_idx <- c(sample(trIdx, length(trIdx) / 2), sample(conIdx,
    length(conIdx) / 2))

train_data <- simulation.1[train_idx, ]

est_data <- simulation.1[-train_idx, ]

honestTree <- honest.causalTree(y ~ x1 + x2 + x3 + x4, data = train_data,
     treatment = train_data$treatment,
     est_data = est_data,
     est_treatment = est_data$treatment,
     split.Rule = "CT", split.Honest = TRUE,
     HonestSampleSize = nrow(est_data),
     split.Bucket = TRUE, cv.option = "CT")

opcp <-  honestTree$cptable[,1][which.min(honestTree$cptable[,4])]

opTree <- prune(honestTree, opcp)

rpart.plot(opTree)

honest re-estimation and change the frame of object using estimation sample

Description

honest re-estimation and change the frame of object using estimation sample

Usage

honest.est.causalTree(fit, x, wt, treatment, y)
honest.est.causalTree(fit, x, wt, treatment, y)

Arguments

`fit`	an `causalTree` object
`x`	input training data
`wt`	optional weights
`treatment`	treatment variable
`y`	outcome variable

Value

An object of class rpart. See rpart.object.

honest re-estimation and change the frame of object using estimation sample

Description

honest re-estimation and change the frame of object using estimation sample

Usage

honest.est.rparttree(fit, x, wt, y)
honest.est.rparttree(fit, x, wt, y)

Arguments

`fit`	an `causalTree` object
`x`	input training data
`wt`	optional weights
`y`	outcome variable

Value

Intermediate estimation results for an honest estimation of causalTree.

Honest recursive partitioning Tree

Description

The recursive partitioning function, for R

Usage

honest.rparttree(
  formula,
  data,
  weights,
  subset,
  est_data,
  est_weights,
  na.action = na.rpart,
  method,
  model = FALSE,
  x = FALSE,
  y = TRUE,
  parms,
  control,
  cost,
  ...
)
honest.rparttree(
  formula,
  data,
  weights,
  subset,
  est_data,
  est_weights,
  na.action = na.rpart,
  method,
  model = FALSE,
  x = FALSE,
  y = TRUE,
  parms,
  control,
  cost,
  ...
)

Arguments

`formula`	a formula, with a response and features but no interaction terms. If this a a data frome, that is taken as the model frame (see `model.frame).`
`data`	an optional data frame that includes the variables named in the formula.
`weights`	optional case weights.
`subset`	optional expression saying that only a subset of the rows of the data should be used in the fit.
`est_data`	data frame to be used for leaf estimates; the estimation sample. Must contain the variables used in training the tree.
`est_weights`	optional case weights for estimation sample
`na.action`	the default action deletes all observations for which `y` is missing, but keeps those in which one or more predictors are missing.
`method`	one of `"anova"`, `"poisson"`, `"class"` or `"exp"`. If `method` is missing then the routine tries to make an intelligent guess. If `y` is a survival object, then `method = "exp"` is assumed, if `y` has 2 columns then `method = "poisson"` is assumed, if `y` is a factor then `method = "class"` is assumed, otherwise `method = "anova"` is assumed. It is wisest to specify the method directly, especially as more criteria may added to the function in future. Alternatively, `method` can be a list of functions named `init`, `split` and `eval`. Examples are given in the file ‘tests/usersplits.R’ in the sources, and in the vignettes ‘User Written Split Functions’.
`model`	model frame of `causalTree`, same as `rpart`
`x`	keep a copy of the `x` matrix in the result.
`y`	keep a copy of the dependent variable in the result. If missing and `model` is supplied this defaults to `FALSE`.
`parms`	optional parameters for the splitting function. Anova splitting has no parameters. Poisson splitting has a single parameter, the coefficient of variation of the prior distribution on the rates. The default value is 1. Exponential splitting has the same parameter as Poisson. For classification splitting, the list can contain any of: the vector of prior probabilities (component `prior`), the loss matrix (component `loss`) or the splitting index (component `split`). The priors must be positive and sum to 1. The loss matrix must have zeros on the diagonal and positive off-diagonal elements. The splitting index can be `gini` or `information`. The default priors are proportional to the data counts, the losses default to 1, and the split defaults to `gini`.
`control`	a list of options that control details of the `rpart` algorithm. See `rpart.control`.
`cost`	a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.
`...`	arguments to `rpart.control` may also be specified in the call to `causalTree`. They are checked against the list of valid arguments. An example of a commonly set parameter would be `xval`, which sets the number of cross-validation samples. The parameter `minsize` is implemented differently in `causalTree` than in `rpart`; we require a minimum of `minsize` treated observations and a minimum of `minsize` control observations in each leaf.

Value

An object of class rpart after running an honest recursive partitioning tree. .

Estimate Heterogeneous Treatment Effect via Causal Tree

Description

Estimate heterogeneous treatment effect via causal tree. In each leaf, the treatment effect is the difference of mean outcome in treatment group and control group.

Usage

hte_causalTree(
  outcomevariable,
  minsize = 20,
  crossvalidation = 20,
  data,
  treatment_indicator,
  ps_indicator,
  covariates,
  negative = FALSE,
  drawplot = TRUE,
  varlabel = NULL,
  maintitle = "Heterogeneous Treatment Effect Estimation",
  legend.x = 0.08,
  legend.y = 0.25,
  check = FALSE,
  ...
)
hte_causalTree(
  outcomevariable,
  minsize = 20,
  crossvalidation = 20,
  data,
  treatment_indicator,
  ps_indicator,
  covariates,
  negative = FALSE,
  drawplot = TRUE,
  varlabel = NULL,
  maintitle = "Heterogeneous Treatment Effect Estimation",
  legend.x = 0.08,
  legend.y = 0.25,
  check = FALSE,
  ...
)

Arguments

`outcomevariable`	a character representing the column name of the outcome variable.
`minsize`	the minimum number of observations in each leaf. The default is set as 20.
`crossvalidation`	number of cross validations. The default is set as 20.
`data`	a data frame containing the variables in the model.
`treatment_indicator`	a character representing the column name of the treatment indicator.
`ps_indicator`	a character representing the column name of the propensity score.
`covariates`	a vector of column names of all covariates (linear terms andpropensity score).
`negative`	a logical value indicating whether we expect the treatment effect to be negative. The default is set as FALSE.
`drawplot`	a logical value indicating whether to plot the model as part of the output. The default is set as TRUE.
`varlabel`	a named vector containing variable labels.
`maintitle`	a character string indicating the main title displayed when plotting the tree and results. The default is set as "Heterogeneous Treatment Effect Estimation".
`legend.x`, `legend.y`	x and y coordinate to position the legend. The default is set as (0.08, 0.25).
`check`	if TRUE, generates 100 trees and outputs most common tree structures and their frequency
`...`	further arguments passed to or from other methods.

Value

predicted treatment effect and the associated tree

Examples

library(rpart)
library(htetree)
hte_causalTree(outcomevariable="outcome",
    data=data.frame("confounder"=c(0, 1, 1, 0, 1, 1),
                    "treatment"=c(0,0,0,1,1,1),
                    "prop_score"=c(0.4, 0.4, 0.5, 0.6, 0.6, 0.7),
                    "outcome"=c(1, 2, 2, 1, 4, 4)),
   treatment_indicator = "treatment",
   ps_indicator = "prop_score",
   covariates = "confounder")
library(rpart)
library(htetree)
hte_causalTree(outcomevariable="outcome",
    data=data.frame("confounder"=c(0, 1, 1, 0, 1, 1),
                    "treatment"=c(0,0,0,1,1,1),
                    "prop_score"=c(0.4, 0.4, 0.5, 0.6, 0.6, 0.7),
                    "outcome"=c(1, 2, 2, 1, 4, 4)),
   treatment_indicator = "treatment",
   ps_indicator = "prop_score",
   covariates = "confounder")

Estimate Heterogeneous Treatment Effect via Random Forest

Description

Estimate heterogeneous treatment effect via random forest. In each leaf, the treatment effect is the difference of mean outcome weighted by inverse propensity scores in treatment group and control group.

Usage

hte_forest(
  outcomevariable,
  minsize = 20,
  crossvalidation = 20,
  data = edurose_mediation_20181126,
  treatment_indicator = "compcoll25",
  ps_indicator = "propsc_com25",
  ps_linear = "propsc_com25lin",
  covariates = c(linear_terms, ps_indicator),
  negative = FALSE,
  drawplot = TRUE,
  legend.x = 0.08,
  legend.y = 0.25,
  gf,
  ...
)
hte_forest(
  outcomevariable,
  minsize = 20,
  crossvalidation = 20,
  data = edurose_mediation_20181126,
  treatment_indicator = "compcoll25",
  ps_indicator = "propsc_com25",
  ps_linear = "propsc_com25lin",
  covariates = c(linear_terms, ps_indicator),
  negative = FALSE,
  drawplot = TRUE,
  legend.x = 0.08,
  legend.y = 0.25,
  gf,
  ...
)

Arguments

`outcomevariable`	a character representing the column name of the outcome variable.
`minsize`	the minimum number of observations in each leaf. The default is set as 20.
`crossvalidation`	number of cross validations. The default is set as 20.
`data`	a data frame containing the variables in the model.
`treatment_indicator`	a character representing the column name of the treatment indicator.
`ps_indicator`	a character representing the column name of the propensity score.
`ps_linear`	a character representing name of a column that stores linearized propensity scores.
`covariates`	a vector of column names of all covariates (linear terms andpropensity score).
`negative`	a logical value indicating whether we expect the treatment effect to be negative. The default is set as FALSE.
`drawplot`	a logical value indicating whether to plot the model as part of the output. The default is set as TRUE.
`legend.x`, `legend.y`	x and y coordinate to position the legend. The default is set as (0.08, 0.25).
`gf`	a fitted generalized random forest object
`...`	further arguments passed to or from other methods.

Value

A list with three elements. The first one is the predicted outcome for each unit. The second is an causalTree object with the tree split information. The third is a data.frame summarizing the prediction results.

Estimate Heterogeneous Treatment Effect via Adjusted Causal Tree

Description

Estimate heterogeneous treatment effect via adjusted causal tree. In each leaf, the treatment effect is the difference of mean outcome weighted by inverse propensity scores in treatment group and control group.

Usage

hte_ipw(
  outcomevariable,
  minsize = 20,
  crossvalidation = 20,
  data,
  treatment_indicator,
  ps_indicator,
  ps_linear = NULL,
  covariates,
  negative = FALSE,
  drawplot = TRUE,
  varlabel = NULL,
  maintitle = "Heterogeneous Treatment Effect Estimation",
  legend.x = 0.08,
  legend.y = 0.25,
  check = FALSE,
  ...
)
hte_ipw(
  outcomevariable,
  minsize = 20,
  crossvalidation = 20,
  data,
  treatment_indicator,
  ps_indicator,
  ps_linear = NULL,
  covariates,
  negative = FALSE,
  drawplot = TRUE,
  varlabel = NULL,
  maintitle = "Heterogeneous Treatment Effect Estimation",
  legend.x = 0.08,
  legend.y = 0.25,
  check = FALSE,
  ...
)

Arguments

`outcomevariable`	a character representing the column name of the outcome variable.
`minsize`	the minimum number of observations in each leaf. The default is set as 20.
`crossvalidation`	number of cross validations. The default is set as 20.
`data`	a data frame containing the variables in the model.
`treatment_indicator`	a character representing the column name of the treatment indicator.
`ps_indicator`	a character representing the column name of the propensity score.
`ps_linear`	a character representing name of a column that stores linearized propensity scores.
`covariates`	a vector of column names of all covariates (linear terms andpropensity score).
`negative`	a logical value indicating whether we expect the treatment effect to be negative. The default is set as FALSE.
`drawplot`	a logical value indicating whether to plot the model as part of the output. The default is set as TRUE.
`varlabel`	a named vector containing variable labels.
`maintitle`	a character string indicating the main title displayed when plotting the tree and results. The default is set as "Heterogeneous Treatment Effect Estimation".
`legend.x`, `legend.y`	x and y coordinate to position the legend. The default is set as (0.08, 0.25).
`check`	if TRUE, generates 100 trees and outputs most common tree structures and their frequency
`...`	further arguments passed to or from other methods.

Value

predicted treatment effect and the associated tree

Examples

library(rpart)
library(htetree)
hte_ipw(outcomevariable="outcome",
data=data.frame("confounder"=c(0, 1, 1, 0, 1, 1),
"treatment"=c(0,0,0,1,1,1), "prop_score"=c(0.4, 0.4, 0.5, 0.6, 0.6, 0.7),
"outcome"=c(1, 2, 2, 1, 4, 4)), treatment_indicator = "treatment",
ps_indicator = "prop_score", covariates = "confounder")
library(rpart)
library(htetree)
hte_ipw(outcomevariable="outcome",
data=data.frame("confounder"=c(0, 1, 1, 0, 1, 1),
"treatment"=c(0,0,0,1,1,1), "prop_score"=c(0.4, 0.4, 0.5, 0.6, 0.6, 0.7),
"outcome"=c(1, 2, 2, 1, 4, 4)), treatment_indicator = "treatment",
ps_indicator = "prop_score", covariates = "confounder")

Estimate Heterogeneous Treatment Effect via Adjusted Causal Tree

Description

Estimate heterogeneous treatment effect via adjusted causal tree. In each leaf, the treatment effect estimated from nn matching.

Usage

hte_match(
  outcomevariable,
  minsize = 20,
  crossvalidation = 20,
  data,
  treatment_indicator,
  ps_indicator,
  ps_linear = NULL,
  covariates,
  negative = FALSE,
  drawplot = TRUE,
  con.num = 1,
  varlabel = NULL,
  maintitle = "Heterogeneous Treatment Effect Estimation",
  legend.x = 0.08,
  legend.y = 0.25,
  check = FALSE,
  ...
)
hte_match(
  outcomevariable,
  minsize = 20,
  crossvalidation = 20,
  data,
  treatment_indicator,
  ps_indicator,
  ps_linear = NULL,
  covariates,
  negative = FALSE,
  drawplot = TRUE,
  con.num = 1,
  varlabel = NULL,
  maintitle = "Heterogeneous Treatment Effect Estimation",
  legend.x = 0.08,
  legend.y = 0.25,
  check = FALSE,
  ...
)

Arguments

`outcomevariable`	a character representing the column name of the outcome variable.
`minsize`	the minimum number of observations in each leaf. The default is set as 20.
`crossvalidation`	number of cross validations. The default is set as 20.
`data`	a data frame containing the variables in the model.
`treatment_indicator`	a character representing the column name of the treatment indicator.
`ps_indicator`	a character representing the column name of the propensity score.
`ps_linear`	a character representing name of a column that stores linearized propensity scores.
`covariates`	a vector of column names of all covariates (linear terms andpropensity score).
`negative`	a logical value indicating whether we expect the treatment effect to be negative. The default is set as FALSE.
`drawplot`	a logical value indicating whether to plot the model as part of the output. The default is set as TRUE.
`con.num`	a number indicating the number of units from control groups to be used in matching.
`varlabel`	a named vector containing variable labels.
`maintitle`	a character string indicating the main title displayed when plotting the tree and results. The default is set as "Heterogeneous Treatment Effect Estimation".
`legend.x`, `legend.y`	x and y coordinate to position the legend. The default is set as (0.08, 0.25).
`check`	if TRUE, generates 100 trees and outputs most common tree structures and their frequency
`...`	further arguments passed to or from other methods.

Value

predicted treatment effect and the associated tree

Examples

library(rpart)
library(htetree)
hte_match(outcomevariable="outcome",
data=data.frame("x1"=c(0, 1, 1, 0, 1, 1),"x2"=c(3, 2, 1, 5, 7, 1),
"treatment"=c(0,0,0,1,1,1), "prop_score"=c(0.4, 0.4, 0.5, 0.6, 0.6, 0.7),
"outcome"=c(1, 2, 2, 1, 4, 4)), treatment_indicator = "treatment",
ps_indicator = "prop_score", covariates = c("x1","x2"))
library(rpart)
library(htetree)
hte_match(outcomevariable="outcome",
data=data.frame("x1"=c(0, 1, 1, 0, 1, 1),"x2"=c(3, 2, 1, 5, 7, 1),
"treatment"=c(0,0,0,1,1,1), "prop_score"=c(0.4, 0.4, 0.5, 0.6, 0.6, 0.7),
"outcome"=c(1, 2, 2, 1, 4, 4)), treatment_indicator = "treatment",
ps_indicator = "prop_score", covariates = c("x1","x2"))

Visualize the Estimated Results

Description

The function hte_plot takes a model created by causal tree, as well as the adjusted version, and plots the distribution of the outcome variable in treated and control groups in each leaf of the tree. This visualization aims to show how the predicted treatment effect changes with each split in the tree.

Usage

hte_plot(
  model,
  data,
  treatment_indicator = NULL,
  outcomevariable,
  propensity_score,
  plot.title = "Visualization of the Tree"
)
hte_plot(
  model,
  data,
  treatment_indicator = NULL,
  outcomevariable,
  propensity_score,
  plot.title = "Visualization of the Tree"
)

Arguments

`model`	a tree model constructed by `hte_causalTree, hte_matchinleaves, or hte_ipw`.
`data`	a data frame containing the variables in the model.
`treatment_indicator`	a character representing the column name for the treatment variable in the causal setup.
`outcomevariable`	a character representing the column name of the outcome variable.
`propensity_score`	a character representing the column name of the propensity score.
`plot.title`	character representing the main title of the plot.

Value

no return value

Visualize the Estimated Results

Description

The function hte_plot_line takes a model created by causal tree, as well as the adjusted version, and plots the different least squares models used to estimate heterogeneous treatment effects(HTE) at each node. At each node, this visualization aims to show how the estimated treatment effect differs when using ordinary least squares and weighted least squares methods. The weighted least squares method in this package uses inverse propensity scores as weights, in order to reduce bias due to confounding variables.

Usage

hte_plot_line(
  model,
  data,
  treatment_indicator = NULL,
  outcomevariable,
  propensity_score,
  plot.title = "Visualization of the Tree",
  gamma = 0,
  lambda = 0,
  ...
)
hte_plot_line(
  model,
  data,
  treatment_indicator = NULL,
  outcomevariable,
  propensity_score,
  plot.title = "Visualization of the Tree",
  gamma = 0,
  lambda = 0,
  ...
)

Arguments

`model`	a tree model constructed by `hte_causalTree, hte_matchinleaves, or hte_ipw`.
`data`	a data frame containing the variables in the model.
`treatment_indicator`	a character representing the column name for the treatment variable in the causal setup.
`outcomevariable`	a character representing the column name of the outcome variable.
`propensity_score`	a character representing the column name of the propensity score.
`plot.title`	character representing the main title of the plot.
`gamma`, `lambda`	numbers indicating the bias level used in sensitivity analysis
`...`	further arguments passed to or from other methods.

Value

No return value, used for plotting the estimated results with lines.

Intermediate function for `causalTree`

Description

Intermediate function for causalTree

Usage

htetree.anova(y, offset, wt)
htetree.anova(y, offset, wt)

Arguments

`y`	outcome variable
`offset`	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be `NULL` or a numeric vector of length equal to the number of cases. One or more offset terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See model.offset.
`wt`	optional weights

Value

No return value.

Caclulate variable importance

Description

Each primary split is credited with the value of splits$improve Each surrogate split gets split$adj times the primary split's value

Usage

importance(fit)
importance(fit)

Arguments

fit

a fitted causalTree object.

Value

same as the importance function in rpart.

Causal Effect Regression and Estimation Forests (Tree Ensembles)

Description

Build a random causal forest by fitting a user selected number of causalTree models to get an ensemble of rpart objects.

Usage

init.causalForest(
  formula,
  data,
  treatment,
  weights = FALSE,
  cost = FALSE,
  num.trees,
  ncov_sample
)

## S3 method for class 'causalForest'
predict(object, newdata, predict.all = FALSE, type = "vector", ...)

causalForest(
  formula,
  data,
  treatment,
  na.action = na.causalTree,
  split.Rule = "CT",
  double.Sample = TRUE,
  split.Honest = TRUE,
  split.Bucket = FALSE,
  bucketNum = 5,
  bucketMax = 100,
  cv.option = "CT",
  cv.Honest = TRUE,
  minsize = 2L,
  propensity,
  control,
  split.alpha = 0.5,
  cv.alpha = 0.5,
  sample.size.total = floor(nrow(data)/10),
  sample.size.train.frac = 0.5,
  mtry = ceiling(ncol(data)/3),
  nodesize = 1,
  num.trees = nrow(data),
  cost = FALSE,
  weights = FALSE,
  ncolx,
  ncov_sample
)
init.causalForest(
  formula,
  data,
  treatment,
  weights = FALSE,
  cost = FALSE,
  num.trees,
  ncov_sample
)

## S3 method for class 'causalForest'
predict(object, newdata, predict.all = FALSE, type = "vector", ...)

causalForest(
  formula,
  data,
  treatment,
  na.action = na.causalTree,
  split.Rule = "CT",
  double.Sample = TRUE,
  split.Honest = TRUE,
  split.Bucket = FALSE,
  bucketNum = 5,
  bucketMax = 100,
  cv.option = "CT",
  cv.Honest = TRUE,
  minsize = 2L,
  propensity,
  control,
  split.alpha = 0.5,
  cv.alpha = 0.5,
  sample.size.total = floor(nrow(data)/10),
  sample.size.train.frac = 0.5,
  mtry = ceiling(ncol(data)/3),
  nodesize = 1,
  num.trees = nrow(data),
  cost = FALSE,
  weights = FALSE,
  ncolx,
  ncov_sample
)

Arguments

`formula`	a formula, with a response and features but no interaction terms. If this a a data frome, that is taken as the model frame (see `model.frame).`
`data`	an optional data frame that includes the variables named in the formula.
`treatment`	a vector that indicates the treatment status of each observation. 1 represents treated and 0 represents control. Only binary treatment supported in this version.
`weights`	optional case weights.
`cost`	a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.
`num.trees`	Number of trees to be built in the causal forest
`ncov_sample`	Number of covariates randomly sampled to build each tree in the forest
`object`	a `causalTree` object
`newdata`	new data to predict
`predict.all`	If TRUE, return predicted individual effect for each observations. Otherwise, return the average effect.
`type`	the type of returned object
`...`	arguments to `rpart.control` may also be specified in the call to `causalForest`. They are checked against the list of valid arguments. The parameter `minsize` is implemented differently in `causalTree` than in `rpart`; we require a minimum of `minsize` treated observations and a minimum of `minsize` control observations in each leaf.
`na.action`	the default action deletes all observations for which `y` is missing, but keeps those in which one or more predictors are missing.
`split.Rule`	causalTree splitting options, one of `"TOT"`, `"CT"`, `"fit"`, `"tstats"`, four splitting rules in `causalTree`. Note that the `"tstats"` alternative does not have an associated cross-validation method `cv.option`; see Athey and Imbens (2016) for a discussion. Note further that `split.Rule` and `cv.option` can mix and match.
`double.Sample`	boolean option, `TRUE` or `FALSE`, if set to True, causalForest will build honest trees.
`split.Honest`	boolean option, `TRUE` or `FALSE`, used to decide the splitting rule of the trees.
`split.Bucket`	boolean option, `TRUE` or `FALSE`, used to specify whether to apply the discrete method in splitting the tree. If set as `TRUE`, in splitting a node, the observations in a leaf will be be partitioned into buckets, with each bucket containing `bucketNum` treated and `bucketNum` control units, and where observations are ordered prior to partitioning. Splitting will take place by bucket.
`bucketNum`	number of observations in each bucket when set `split.Bucket` = `TRUE`. However, the code will override this choice in order to guarantee that there are at least `minsize` and at most `bucketMax` buckets.
`bucketMax`	Option to choose maximum number of buckets to use in splitting when set `split.Bucket` = `TRUE`, `bucketNum` can change by choice of `bucketMax`.
`cv.option`	cross validation options, one of `"TOT"`, `"matching"`, `"CT"`, `"fit"`, four cross validation methods in causalTree. There is no `cv.option` for the `split.Rule` `"tstats"`; see Athey and Imbens (2016) for discussion.
`cv.Honest`	boolean option, `TRUE` or `FALSE`, only used for `cv.option` as `"CT"` or `"fit"`, to specify whether to apply honest risk evalation function in cross validation. If set `TRUE`, use honest risk function, otherwise use adaptive risk function in cross validation. If set `FALSE`, the user choice of `cv.alpha` will be set to 1. If set `TRUE`, `cv.alpha` will default to 0.5, but the user choice of `cv.alpha` will be respected. Note that honest cv estimates within-leaf variances and may perform better with larger leaf sizes and/or small number of cross-validation sets.
`minsize`	in order to split, each leaf must have at least `minsize` treated cases and `minsize` control cases. The default value is set as 2.
`propensity`	propensity score used in `"TOT"` splitting and `"TOT"`, honest `"CT"` cross validation methods. The default value is the proportion of treated cases in all observations. In this implementation, the propensity score is a constant for the whole dataset. Unit-specific propensity scores are not supported; however, the user may use inverse propensity scores as case weights if desired.
`control`	a list of options that control details of the `rpart` algorithm. See `rpart.control`.
`split.alpha`	scale parameter between 0 and 1, used in splitting risk evaluation function for `"CT"`. When `split.Honest = FALSE`, `split.alpha` will be set as 1. For `split.Rule`=`"tstats"`, if `split.Honest`=`TRUE`, `split.alpha` is used in calculating the risk function, which determines the order of pruning in cross-validation.
`cv.alpha`	scale paramter between 0 and 1, used in cross validation risk evaluation function for `"CT"` and `"fit"`. When `cv.Honest = FALSE`, `cv.alpha` will be set as 1.
`sample.size.total`	Sample size used to build each tree in the forest (sampled randomly with replacement).
`sample.size.train.frac`	Fraction of the sample size used for building each tree (training). For eexample, if the sample.size.total is 1000 and frac =0.5 then, 500 samples will be used to build the tree and the other 500 samples will be used the evaluate the tree.
`mtry`	Number of data features used to build a tree (This variable is not used presently).
`nodesize`	Minimum number of observations for treated and control cases in one leaf node
`ncolx`	Total number of covariates

Details

CausalForest builds an ensemble of CausalTrees (See Athey and Imbens, Recursive Partitioning for Heterogeneous Causal Effects (2016)), by repeated random sampling of the data with replacement. Further, each tree is built using a randomly sampled subset of all available covariates. A causal forest object is a list of trees. To predict, call R's predict function with new test data and the causalForest object (estimated on the training data) obtained after calling the causalForest function. During the prediction phase, the average value over all tree predictions is returned as the final prediction by default. To return the predictions of each tree in the forest for each test observation, set the flag predict.all=TRUE CausalTree differs from rpart function from rpart package in splitting rules and cross validation methods. Please check Athey and Imbens, Recursive Partitioning for Heterogeneous Causal Effects (2016) and Stefan Wager and Susan Athey, Estimation and Inference of Heterogeneous Treatment Effects using Random Forests for more details.

Value

An object of class rpart. See rpart.object.

References

Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.

Athey, S and G Imbens (2016) Recursive Partitioning for Heterogeneous Causal Effects. http://arxiv.org/abs/1504.01132

Wager,S and Athey, S (2015) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests http://arxiv.org/abs/1510.04342

Examples

library(rpart)
library("htetree")
cf <- causalForest(y~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10, data=simulation.1,
  treatment=simulation.1$treatment,
  split.Rule="CT", split.Honest=TRUE,
  split.Bucket=FALSE, bucketNum = 5,
  bucketMax = 100, cv.option="CT", cv.Honest=TRUE, minsize = 2L,
  split.alpha = 0.5, cv.alpha = 0.5,
  sample.size.total = floor(nrow(simulation.1) / 2),
  sample.size.train.frac = .5,
  mtry = ceiling(ncol(simulation.1)/3), nodesize = 3, num.trees= 5,
  ncolx=10,ncov_sample=3)

cfpredtest <- predict.causalForest(cf, newdata=simulation.1[1:100,],
  type="vector")
library(rpart)
library("htetree")
cf <- causalForest(y~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10, data=simulation.1,
  treatment=simulation.1$treatment,
  split.Rule="CT", split.Honest=TRUE,
  split.Bucket=FALSE, bucketNum = 5,
  bucketMax = 100, cv.option="CT", cv.Honest=TRUE, minsize = 2L,
  split.alpha = 0.5, cv.alpha = 0.5,
  sample.size.total = floor(nrow(simulation.1) / 2),
  sample.size.train.frac = .5,
  mtry = ceiling(ncol(simulation.1)/3), nodesize = 3, num.trees= 5,
  ncolx=10,ncov_sample=3)

cfpredtest <- predict.causalForest(cf, newdata=simulation.1[1:100,],
  type="vector")

Visualize Causal Tree and the Estimated Results

Description

An intermediate function used for plotting

Usage

makeplots(
  negative,
  opfit. = opfit,
  trainset,
  covariates,
  outcomevariable,
  data. = data,
  hte_effect_setup,
  varlabel,
  maintitle,
  legend.x = 0.8,
  legend.y = 0.25,
  ...
)
makeplots(
  negative,
  opfit. = opfit,
  trainset,
  covariates,
  outcomevariable,
  data. = data,
  hte_effect_setup,
  varlabel,
  maintitle,
  legend.x = 0.8,
  legend.y = 0.25,
  ...
)

Arguments

`negative`	a logical value indicating whether we expect the treatment effect to be negative. The default is set as FALSE.
`opfit.`	tree structure generated from causal tree algorithm.
`trainset`	a data frame only containing the variables used in the model and missings values are listwise deleted.
`covariates`	a vector of column names of all covariates (linear terms andpropensity score).
`outcomevariable`	a character representing the column name of the outcome variable.
`data.`	a data frame containing the variables in the model.
`hte_effect_setup`	a empty list to store the adjusted treatment effect.
`varlabel`	a named vector containing variable labels.
`maintitle`	a character string indicating the main title displayed when plotting the tree and results. The default is set as "Heterogeneous Treatment Effect Estimation".
`legend.x`, `legend.y`	x and y coordinate to position the legend. The default is set as (0.08, 0.25).
`...`	further arguments passed to or from other methods.

Value

A plot visualizing the tree and estimated treatment effect in each node.

NN Matching in Leaves

Description

This intermediate function is used to adjust the heterogeneous treatment effect estimated in each leaf with NN matching.

Usage

matchinleaves(
  trainset = match_data,
  covariates = covariates,
  outcomevariable = outcomevariable,
  hte_effect_setup = hte_effect_setup,
  treatment_indicator,
  con.num = 1,
  ...
)
matchinleaves(
  trainset = match_data,
  covariates = covariates,
  outcomevariable = outcomevariable,
  hte_effect_setup = hte_effect_setup,
  treatment_indicator,
  con.num = 1,
  ...
)

Arguments

`trainset`	a data frame only containing the variables used in the model and missings values are listwise deleted.
`covariates`	a vector of column names of all covariates (linear terms andpropensity score).
`outcomevariable`	a character representing the column name of the outcome variable.
`hte_effect_setup`	a empty list to store the adjusted treatment effect.
`treatment_indicator`	a character representing the column name of the treatment indicator.
`con.num`	a number indicating the number of units from control groups to be used in matching
`...`	further arguments passed to or from other methods.

Value

A list for summarizing the results after matching.

Intermediate function for `causalTree`

Description

get model frame of causalTree, same as rpart

Usage

## S3 method for class 'causalTree'
model.frame(formula, ...)
## S3 method for class 'causalTree'
model.frame(formula, ...)

Arguments

formula

a formula, with a response but no interaction terms. If this is a data frame, it is taken as the model frame (see model.frame).

...

arguments to rpart.control may also be specified in the call to causalTree. They are checked against the list of valid arguments. An example of a commonly set parameter would be xval, which sets the number of cross-validation samples. The parameter minsize is implemented differently in causalTree than in rpart; we require a minimum of minsize treated observations and a minimum of minsize control observations in each leaf.

Value

a model frame for causalTree.

Intermediate function for `causalTree`

Description

requirement when missing values are included in sample.

Usage

na.causalTree(x)
na.causalTree(x)

Arguments

`x`	covariates

Value

No return value, used for handling missing values when thy are included in sample.

Intermediate function for `hte_plot_line`

Description

Plots the different least squares models used to estimate heterogeneous treatment effects(HTE) at each node. At each node, this visualization aims to show how the estimated treatment effect differs when using ordinary least squares and weighted least squares methods. The weighted least squares method in this package uses inverse propensity scores as weights, in order to reduce bias due to confounding variables.

Usage

plotOutcomes(
  treatment,
  outcome,
  propscores,
  confInt = TRUE,
  colbyWt = FALSE,
  ylab = "",
  xlab = "",
  title = "",
  gamma = 0,
  lambda = 0,
  ...
)
plotOutcomes(
  treatment,
  outcome,
  propscores,
  confInt = TRUE,
  colbyWt = FALSE,
  ylab = "",
  xlab = "",
  title = "",
  gamma = 0,
  lambda = 0,
  ...
)

Arguments

`treatment`	a character representing the column name for the treatment variable in the causal setup
`outcome`	a character representing the column name of the outcome variable.
`propscores`	a character representing the column name of the propensity score.
`confInt`	a logical value indicating whether adding the 95 confidence interval. The default is set as `TRUE`.
`colbyWt`	a logical value indicating whether the points are are colored according to inverse propensity scores. The default is set as FALSE.
`xlab`, `ylab`, `title`	Characters representing the name for x axis, y axis, and main title for each node.
`gamma`, `lambda`	numbers indicating the bias level used in sensitivity analysis
`...`	further arguments passed to or from other methods.

Value

A summary table after adjusting the estimates with inverse probability weighting (ipw).

Visualize Causal Tree and Treatment Effects via Shiny

Description

Visualize Causal Tree and Treatment Effects via Shiny

Usage

runDynamic(
  model,
  data,
  outcomevariable,
  treatment_indicator,
  propensity_score = ""
)
runDynamic(
  model,
  data,
  outcomevariable,
  treatment_indicator,
  propensity_score = ""
)

Arguments

`model`	a tree model constructed by `hte_causalTree, hte_matchinleaves, or hte_ipw`.
`data`	a data frame containing the variables in the model.
`outcomevariable`	a character representing the column name of the outcome variable.
`treatment_indicator`	a character representing the column name for the treatment variable in the causal setup.
`propensity_score`	a character representing the column name of the propensity score.

Value

a Shiny page.

Save Javascript Embedded in Shiny App

Description

Save Javascript Embedded in Shiny App

Usage

saveBCSS(filePath)
saveBCSS(filePath)

Arguments

filePath

a character string representing the path name to save the files temporarily.

Value

No return value. It is used to save necessary files temporarily to run Shiny App.

Save Necessary Files to Run Shiny App

Description

This function is to save files necessary to run Shiny app to visualize causal tree and the estimated heterogeneous treatment effects in an interactive way.

Usage

saveFiles(
  model,
  data,
  outcomevariable,
  treatment_indicator,
  propensity_score = "",
  filePath = ""
)
saveFiles(
  model,
  data,
  outcomevariable,
  treatment_indicator,
  propensity_score = "",
  filePath = ""
)

Arguments

`model`	a tree model constructed by `hte_causalTree, hte_matchinleaves, or hte_ipw`.
`data`	a data frame containing the variables in the model.
`outcomevariable`	a character representing the column name of the outcome variable.
`treatment_indicator`	a character representing the column name for the treatment variable in the causal setup.
`propensity_score`	a character representing the column name of the propensity score.
`filePath`	a character string representing the path name to save the files temporarily.

Value

No return value. It is used to save necessary files temporarily to run Shiny App.

Save CSS File Embedded in Shiny App

Description

Save CSS File Embedded in Shiny App

Usage

saveGCSS(filePath)
saveGCSS(filePath)

Arguments

filePath

a character string representing the path name to save the files temporarily.

Value

No return value. It is used to save necessary files temporarily to run Shiny App.

Save HTML Index Embedded in Shiny App

Description

Save HTML Index Embedded in Shiny App

Usage

saveInd(filePath)
saveInd(filePath)

Arguments

filePath

a character string representing the path name to save the files temporarily.

Value

No return value. It is used to save necessary files temporarily to run Shiny App.

Save Shiny Server Temporarily

Description

Save Shiny Server Temporarily

Usage

saveServ(filePath)
saveServ(filePath)

Arguments

filePath

a character string representing the path name to save the files temporarily.

Value

No return value. It is used to save necessary files temporarily to run Shiny App.

Save Shiny UI Temporarily

Description

Save Shiny UI Temporarily

Usage

saveUI(filePath)
saveUI(filePath)

Arguments

filePath

a character string representing the path name to save the files temporarily.

Value

No return value. It is used to save necessary files temporarily to run Shiny App.

A Simulated Dataset

Description

A simulated dataset inherited from causalTree package

Usage

simulation.1
simulation.1

Format

## 'simulation.1' A data frame with 500 observations on the following 12 variables.

x1: a numeric vector
x2: a numeric vector
x3: a numeric vector
x4: a numeric vector
x5: a numeric vector
x6: a numeric vector
x7: a numeric vector
x8: a numeric vector
x9: a numeric vector
x10: a numeric vector
y: a numeric vector
treatment: a numeric vector

Package 'htetree'

Help Index

Include the Javascript Used in Shiny

Description

Usage

Arguments

Value

Causal Effect Regression and Estimation Trees

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Compute the "branches" to be drawn for an causalTree object

Description

Usage

Arguments

Value

Intermediate function for causalTree

Description

Usage

Arguments

Value

Intermediate function for causalTree

Description

Usage

Arguments

Value

Intermediate function for causalTree

Description

Usage

Arguments

Value

Intermediate function for causalTree

Description

Usage

Arguments

Value

Clear Temporary Files

Description

Usage

Value

Intermediate function for causalTree

Description

Usage

Arguments

Value

estimate causal Tree

Description

Usage

Arguments

Details

Value

Intermediate function for causalTree

Description

Usage

Arguments

Value

Get the Current Working Directory

Description

Usage

Value

Getting Distribution in Treatment and Control Groups

Description

Usage

Arguments

Value

Causal Effect Regression and Estimation Trees: One-step honest estimation

Description

Usage

Arguments

Value

References

See Also

Examples

honest re-estimation and change the frame of object using estimation sample

Description

Compute the "branches" to be drawn for an `causalTree` object

Intermediate function for `causalTree`

Intermediate function for `causalTree`

Intermediate function for `causalTree`

Intermediate function for `causalTree`

Intermediate function for `causalTree`

Intermediate function for `causalTree`

Intermediate function for `causalTree`