Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks

TL;DR

This paper introduces a novel deep neural network approach within extended fiducial inference for estimating individual treatment effects, demonstrating superior performance and advancing statistical inference theory for large models.

Contribution

It proposes the Double-NN method combining neural networks for treatment effect modeling with theoretical guarantees for model size and uncertainty quantification.

Findings

Double-NN outperforms conformal quantile regression in experiments.

Theoretical proof allows model size to grow with sample size at rate O(n^ζ).

Provides a framework for uncertainty quantification in large neural networks.

Abstract

Individual treatment effect estimation has gained significant attention in recent data science literature. This work introduces the Double Neural Network (Double-NN) method to address this problem within the framework of extended fiducial inference (EFI). In the proposed method, deep neural networks are used to model the treatment and control effect functions, while an additional neural network is employed to estimate their parameters. The universal approximation capability of deep neural networks ensures the broad applicability of this method. Numerical results highlight the superior performance of the proposed Double-NN method compared to the conformal quantile regression (CQR) method in individual treatment effect estimation. From the perspective of statistical inference, this work advances the theory and methodology for statistical inference of large models. Specifically, it is theoretically proven that the proposed method permits the model size to increase with the sample size $n$ at a rate of $O(n^{\zeta})$ for some $0 \leq \zeta<1$, while still maintaining proper quantification of uncertainty in the model parameters. This result marks a significant improvement compared to the range $0\leq \zeta < \frac{1}{2}$ required by the classical central limit theorem. Furthermore, this work provides a rigorous framework for quantifying the uncertainty of deep neural networks under the neural scaling law, representing a substantial contribution to the statistical understanding of large-scale neural network models.