estimate.causalTree: estimate causal Tree

In htetree: Causal Inference with Tree-Based Machine Learning Algorithms

estimate causal Tree

Description

estimate causal Tree

Usage

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

htetree documentation built on April 4, 2025, 5:15 a.m.