R: Causal Effect Regression and Estimation Trees: One-step...

honest.causalTree {htetree}

R Documentation

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,
  ...
)

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)

[Package htetree version 0.1.18 Index]