aipw_rf_att: Estimate the average treatment effect on the treated (ATT)...

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

Description

Estimate the average treatment effect on the treated (ATT) via augmented inverse propensity weighting.

Usage

aipw_rf_att(data, x, y, w, p, 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.
p	a vector containing propensity score values.
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 augmented inverse propensity score weighted estimate on just the treated group (see aipw_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

Rotnitzky, Andrea, James M. Robins, and Daniel O. Scharfstein. 1998. “Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse." Journal of the American Statistical Association. Vol. 93, No. 444, Dec. pgs. 1321-1339.

Cao, Weihua, Anastasios A. Tsiatis, and Marie Davidian. 2009. “Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data". Biometrika. Vol. 96(3). pgs. 723–734.

Examples

data("lalonde")

# calculate propensity scores
p <- propensity_score(lalonde, y = "re78", w = "treat", model = "logistic")
att <- aipw_rf_att(data = lalonde, y = "re78", w = "treat", p = p, num.trees = 100, mtry = 3)

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