Causal Estimation of Exposure Shifts with Neural Networks

TL;DR

This paper introduces TRESNET, a neural network method with theoretical guarantees for estimating the effects of exposure shifts in causal inference, applicable to various outcome types and demonstrated on public health policy data.

Contribution

We develop a targeted regularization loss for neural networks that ensures double robustness and efficiency for shift-response function estimation, extending to non-continuous outcomes.

Findings

TRESNET achieves broad applicability and competitive performance in benchmark tests.

It provides reliable estimates of causal effects under exposure shifts.

Applied to US air quality standards, it estimates significant health impact reductions.

Abstract

A fundamental task in causal inference is estimating the effect of distribution shift in the treatment variable. We refer to this problem as shift-response function (SRF) estimation. Existing neural network methods for causal inference lack theoretical guarantees and practical implementations for SRF estimation. In this paper, we introduce Targeted Regularization for Exposure Shifts with Neural Networks (TRESNET), a method to estimate SRFs with robustness and efficiency guarantees. Our contributions are twofold. First, we propose a targeted regularization loss for neural networks with theoretical properties that ensure double robustness and asymptotic efficiency specific to SRF estimation. Second, we extend targeted regularization to support loss functions from the exponential family to accommodate non-continuous outcome distributions (e.g., discrete counts). We conduct benchmark experiments demonstrating TRESNET's broad applicability and competitiveness. We then apply our method to a key policy question in public health to estimate the causal effect of revising the US National Ambient Air Quality Standards (NAAQS) for PM 2.5 from 12 ${\mu}g/m^3$ to 9 ${\mu}g/m^3$. This change has been recently proposed by the US Environmental Protection Agency (EPA). Our goal is to estimate the reduction in deaths that would result from this anticipated revision using data consisting of 68 million individuals across the U.S.