psw.wt: Propensity score weighting estimator

In PSW: Propensity Score Weighting Methods for Dichotomous Treatments

Description

psw.wt is used to estimate the weighted treatment effect estimator (without double robustness).

Usage

psw.wt(data, form.ps, weight, out.var, family = "gaussian", K = 4)

Arguments

data	data frame to be used.
form.ps	propensity score model.
weight	weighting method to be used. Available methods are "ATE", "ATT", "ATC", "MW", "OVERLAP", and "TRAPEZOIDAL".
out.var	outcome variable.
family	outcome family, either "gaussian" or "binomial". family="gaussian" by default.
K	value of K in ω(e_i) = min(1, K min(e_i, 1-e_i)) for "TRAPEZOIDAL" weight. The estimand is closer to the average treatment effect (ATE) with larger value of K. K=4 by default.

Details

psw.wt is used to estimate the weighted estimator, \hat{Δ}, and make inference using the sandwich variance estimator that takes into account the sampling variability in the estimated propensity score.

Value

A list of weighting method, fitted propensity score model, estimated propenstity scores, estimated propensity score weights, weighted estimator and standard error estimator

weight	weighting method.
ps.model	object returned by fitting the propensity score model using glm with "binomial" family.
ps.hat	estimated propensity score.
W	estimated propensity score weight.
est.wt	weighted estimator for mean difference when family = "gaussian".
std.wt	standard error for est.wt.
est.risk.wt	weighted estimator for risk difference when family = "binomial".
std.risk.wt	standard error for est.risk.wt.
est.rr.wt	weighted estimator for relative risk when family = "binomial".
std.rr.wt	standard error for est.rr.wt.
est.or.wt	weighted estimator for odds ratio when family = "binomial".
std.or.wt	standard error for est.or.wt.
est.lor.wt	weighted estimator for log odds ratio when family = "binomial".
std.lor.wt	standard error for est.lor.wt.

See Also

psw

Examples

# Load the test data set
data(test_data);
# Propensity score model
form.ps <- "Z ~ X1 + X2 + X3 + X4";
tmp <- psw.wt( data = test_data, weight = "ATE", form.ps = form.ps,
out.var = "Y", family = "gaussian" );

PSW documentation built on May 2, 2019, 6:01 a.m.