Tuning-parameter-free propensity score matching approach for causal inference under shape restriction

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 approach to causal inference 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 causal effect estimator is asymptotically semiparametric efficient when the covariate is univariate or the outcome and the propensity score depend on the covariate through the same index $X^{\top }\beta$. We conclude that matching methods based on the propensity score alone cannot, in general, be efficient.