Causal Inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders

Abstract

Semiparametric efficient estimation of various multi-valued causal effects, including quantile treatment effects, is important in economic, biomedical, and other social sciences. Under the unconfoundedness condition, adjustment for confounders requires estimating the nuisance functions relating outcome or treatment to confounders nonparametrically. This paper considers a generalized optimization framework for efficient estimation of general treatment effects using artificial neural networks (ANNs) to approximate the unknown nuisance function of growing-dimensional confounders. We establish a new approximation error bound for the ANNs to the nuisance function belonging to a mixed smoothness class without a known sparsity structure. We show that the ANNs can alleviate the "curse of dimensionality" under this circumstance. We establish the root-$n$ consistency and asymptotic normality of the proposed general treatment effects estimators, and apply a weighted bootstrap procedure for conducting inference. The proposed methods are illustrated via simulation studies and a real data application.

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• Semiparametric efficiency in GMM models with auxiliary data
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Cited by in corpus (3)

• Deep Quantile Regression: Mitigating the Curse of Dimensionality Through Composition
• Covariate-adjusted Fisher randomization tests for the average treatment effect
• Quantile regression with deep ReLU Networks: Estimators and minimax rates