estimate_cate: Estimate the Conditional Average Treatment Effect

In CRE: Interpretable Discovery and Inference of Heterogeneous Treatment Effects

Estimate the Conditional Average Treatment Effect

Description

Estimates the Conditional Average Treatment Effect (CATE) by linearly modeling the Individual Treatment Effect by a set of rules.

Usage

estimate_cate(rules_matrix, rules_explicit, ite, B = 1, subsample = 1)

Arguments

rules_matrix	A rules matrix.
rules_explicit	A list of select rules in terms of covariate names.
ite	The estimated ITEs.
B	The number of bootstrap samples for uncertainty quantification in estimation.
subsample	The bootstrap ratio subsample for uncertainty quantification in estimation.

Value

A list with 2 elements: summary: A data frame summarizing the CATE linear decomposition:

• Rule: rule name,

• Estimate: linear contribution to CATE,

• CI_lower: lower bound 95% confidence interval on the estimate,

• CI_upper: upper bound 95% confidence interval on the estimate,

• P_Value: p-value (from Z-test). model: A linear model for CATE-ATE estimation.

CRE documentation built on Oct. 19, 2024, 5:07 p.m.