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This paper investigates the estimation and inference of the average treatmenteffect (ATE) using deep neural networks (DNNs) in the potential outcomesframework. Under some regularity conditions, the observed response can beformulated as the response of a mean regression problem with both theconfounding variables and the treatment indicator as the independent variables.Using such formulation, we investigate two methods for ATE estimation andinference based on the estimated mean regression function via DNN regressionusing a specific network architecture. We show that both DNN estimates of ATEare consistent with dimension-free consistency rates under some assumptions onthe underlying true mean regression model. Our model assumptions accommodatethe potentially complicated dependence structure of the observed response onthe covariates, including latent factors and nonlinear interactions between thetreatment indicator and confounding variables. We also establish the asymptoticnormality of our estimators based on the idea of sample splitting, ensuringprecise inference and uncertainty quantification. Simulation studies and realdata application justify our theoretical findings and support our DNNestimation and inference methods.

Dimension-Free Average Treatment Effect Inference with Deep Neural Networks