Flexible semiparametric modeling with application to Causal Inference

TL;DR

This paper introduces a flexible framework for constructing Neyman-orthogonal scores in semiparametric models, facilitating robust causal inference and integrating machine learning techniques.

Contribution

It provides explicit construction strategies for orthogonal scores in complex models, ensuring asymptotic normality regardless of nuisance estimator methods.

Findings

The framework guarantees asymptotic normality under mild conditions.

Develops a robust estimator for treatment effects with binary instrumental variables.

Numerical studies show significant finite-sample performance improvements.

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.