$C_p$ criterion for semiparametric approach in causal inference

TL;DR

This paper introduces a new $C_p$ criterion for model selection in semiparametric causal inference models, specifically for marginal structural models, addressing the lack of classical criteria like AIC.

Contribution

The paper develops an asymptotic unbiased estimator of mean squared error as a $C_p$ criterion tailored for semiparametric causal inference models, which was previously unavailable.

Findings

Proposed criterion outperforms existing criteria in simulations.

It achieves smaller squared errors and higher true model selection frequency.

Real data analysis shows clear differences in model choices using the new criterion.

Abstract

For marginal structural models, which recently play an important role in causal inference, we consider a model selection problem in the framework of a semiparametric approach using inverse-probability-weighted estimation or doubly robust estimation. In this framework, the modeling target is a potential outcome which may be a missing value, and so we cannot apply the AIC nor its extended version to this problem. In other words, there is no analytical information criterion obtained according to its classical derivation for this problem. Hence, we define a mean squared error appropriate for treating the potential outcome, and then we derive its asymptotic unbiased estimator as a $C_{p}$ criterion from an asymptotics for the semiparametric approach and using an ignorable treatment assignment condition. In simulation study, it is shown that the proposed criterion exceeds a conventionally derived existing criterion in the squared error and model selection frequency. Specifically, in all simulation settings, the proposed criterion provides clearly smaller squared errors and higher frequencies selecting the true or nearly true model. Moreover, in real data analysis, we check that there is a clear difference between the selections by the two criteria.