rf_att: Uses random forests to naively estimate the average treatment...

In jackcollison/causality: Tools for Performing Causal Inference with Observational Data

Description

Uses random forests to naively estimate the average treatment effect on the treated (ATT) without weighting

Usage

rf_att(data, x, y, w, cf = TRUE, ...)

Arguments

data	a dataframe object containing the variables and values.
x	a list of character vectors specifying variables to be included in the model (columns in the data). If unspecified, then it is assumed to be all columns in the data besides y and w.
y	a character vector specifying the response variable.
w	a character vector specifying the treatment status.
cf	logical; if TRUE then includes confidence interval on ATT.
...	additional arguments to causal_forest.

Details

Computes an estimate of the ATT τ_T with a naive estimate on just the treated group (see naive_ate for more details).

Value

a list of ATT, 95 percent confidence interval upperbound and lowerbound or just ATT, depending on user input of cf.

References

Athey, Susan, Imbens, Guido, Pham, Thai, and Wager, Stefan. 2017. “Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges." The American Economic Review, Vol. 107(5). pgs. 278–281. urlwww.jstor.org/stable/44250405

Examples

data("lalonde")
att <- rf_att(data = lalonde, y = "re78", w = "treat", num.trees = 100, mtry = 3)

jackcollison/causality documentation built on Dec. 20, 2021, 8:05 p.m.