Flexible semiparametric modeling with application to Causal Inference

2026-04-29

AI Summary

This study addresses the challenge of constructing Neyman-orthogonal scores for robust causal inference in semiparametric models with infinite-dimensional nuisance parameters. The authors propose a general framework that, for the first time, explicitly constructs orthogonal scores for a broad class of such models, yielding estimators of the target parameter that are asymptotically normal and require only a convergence rate of $o_p(n^{-1/4})$ or better for the nuisance parameter estimates. The approach seamlessly integrates with machine learning algorithms and is applied to estimate causal effects under binary instrumental variables. Numerical experiments demonstrate substantial finite-sample improvements over naive estimators, and an empirical analysis of the Oregon Health Insurance Experiment confirms the method’s robustness and practical utility in real-world settings.

Abstract

This paper proposes a flexible new framework for constructing Neyman-orthogonal scores in semiparametric models involving infinite-dimensional nuisance parameters. While locally estimation is vital for integrating machine learning into econometrics, deriving orthogonal scores for complex models remains a major challenge. We provide explicit construction strategies for broad classes of settings. The proposed framework ensures asymptotic normality of target parameter estimators in a way that does not depend on the method used to construct the nuisance parameter estimators, provided they are $o_p(n^{-\1/4})$-consistent. We apply the proposed methodology to causal inference with a binary instrumental variable, developing a novel, robust estimator for treatment effects. Numerical studies demonstrate that our approach significantly outperforms naive alternatives in finite samples. An empirical application to the Oregon Health Insurance Experiment illustrates the framework's utility in providing robust causal evidence.

Problem

• semiparametric models
• Neyman-orthogonal scores
• causal inference
• nuisance parameters
• instrumental variable

Innovation

• Neyman-orthogonal scores
• semiparametric modeling
• causal inference
• machine learning in econometrics
• robust estimation