ps_glm: Fitting Logistic Regression for Propensity Score

In ygeunkim/propensityml: Propensity score weighting using machine learning methods

Description

fits logistic regression model before estimating propensity scores

Usage

ps_glm(formula, data, ...)

Arguments

formula	an object formula to be fitted. Response should be treatment.
data	optional data frame.
...	For additional options of glm.

Details

Propensity score is

e(X) = P(Z_i = 1 \mid X_i = x)

, which is the conditional probability of receiving treatment. Naturally, logit model is the easiest way to estimate the score.

Value

propmod class, a list with model and its name

• model - glm model

• name - "glm"

• data - dataset

References

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. https://doi.org/10.1093/biomet/70.1.41

Examples

fit <- chemical %>% ps_glm(poisox ~ age + sex, data = .)

ygeunkim/propensityml documentation built on Jan. 1, 2021, 1:44 p.m.