On true versus estimated propensity scores for treatment effect estimation with discrete controls

Andrew Herren; P. Richard Hahn

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arXiv · 2305.11163

On true versus estimated propensity scores for treatment effect estimation with discrete controls

Andrew Herren·P. Richard Hahn

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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.

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Andrew Herren, P. Richard Hahn. 2023-05-18. On true versus estimated propensity scores for treatment effect estimation with discrete controls. https://arxiv.org/abs/2305.11163

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