On true versus estimated propensity scores for treatment effect estimation with discrete controls
Year: 2023
Type: preprint
Abstract: The finite sample variance of an inverse propensity weighted estimator is derived in the case of discrete control variables with finite support. The obtained expressions generally corroborate widely-cited asymptotic theory showing that estimated propensity scores are superior to true propensity scores in the context of inverse propensity weighting. However, similar analysis of a modified estimator demonstrates that foreknowledge of the true propensity function can confer a statistical advantage when estimating average treatment effects.
Source: arXiv (Cornell University)
Authors: Herren, Andrew, Hahn, P. Richard
Institution: Arizona State University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4377164377