prop_weighted_ols_ate: Estimate average treatment effect (ATE) with inverse...

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

Description

Estimate average treatment effect (ATE) with inverse propensity score weighting using OLS.

Usage

prop_weighted_ols_ate(data, y, w, p, cf = TRUE)

Arguments

data	a dataframe object containing the variables and values.
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 ATE.

Details

Computes an estimate of the ATE τ via weighted OLS regression using propensity score weighting The weights are given by:

w_i = \frac{W_i}{e(X_i)} - \frac{1 - W_i}{1 - e(X_i)}

where e(X_i) = P(W_i = 1 | X_i = x) is the propensity score.

Value

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

References

Aronow, Peter M.; Samii, Cyrus. “Estimating average causal effects under general interference, with application to a social network experiment." Annals of Applied Statistics 11 (2017), no. 4, 1912–1947 https://arxiv.org/pdf/1305.6156.pdf

Examples

data("lalonde")

# calculate propensity scores
p <- propensity_score(lalonde, y = "re78", w = "treat", model = "logistic")
ate <- prop_weighted_ols_ate(data = lalonde, y = "re78", w = "treat", p = p)

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