Tuning-parameter-free optimal propensity score matching approach for causal inference

TL;DR

This paper introduces a tuning-parameter-free propensity score matching method that automatically determines the optimal number of matches, improving causal inference without the need for parameter tuning.

Contribution

It proposes a nonparametric maximum-likelihood estimation approach for propensity scores that is tuning-parameter-free and asymptotically efficient, addressing a key gap in PSM methodology.

Findings

The method is asymptotically semiparametric efficient in univariate cases.

It achieves efficiency in multivariate cases when outcome and propensity depend similarly on covariates.

Matching based solely on propensity scores may not always be efficient.

Abstract

Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or more untreated units of similar characteristics. To date, the problem of determining the optimal number of matches per unit, which plays an important role in PSM, has not been adequately addressed. We propose a tuning-parameter-free PSM method based on the nonparametric maximum-likelihood estimation of the propensity score under the monotonicity constraint. The estimated propensity score is piecewise constant, and therefore automatically groups data. Hence, our proposal is free of tuning parameters. The proposed estimator is asymptotically semiparametric efficient for the univariate case, and achieves this level of efficiency in the multivariate case when the outcome and the propensity score depend on the covariate in the same direction. We conclude that matching methods based on the propensity score alone cannot, in general, be efficient.