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A fundamental task in causal inference is estimating the effect ofdistribution shift in the treatment variable. We refer to this problem asshift-response function (SRF) estimation. Existing neural network methods forcausal inference lack theoretical guarantees and practical implementations forSRF estimation. In this paper, we introduce Targeted Regularization forExposure Shifts with Neural Networks (TRESNET), a method to estimate SRFs withrobustness and efficiency guarantees. Our contributions are twofold. First, wepropose a targeted regularization loss for neural networks with theoreticalproperties that ensure double robustness and asymptotic efficiency specific toSRF estimation. Second, we extend targeted regularization to support lossfunctions from the exponential family to accommodate non-continuous outcomedistributions (e.g., discrete counts). We conduct benchmark experimentsdemonstrating TRESNET's broad applicability and competitiveness. We then applyour method to a key policy question in public health to estimate the causaleffect of revising the US National Ambient Air Quality Standards (NAAQS) for PM2.5 from 12 ${\mu}g/m^3$ to 9 ${\mu}g/m^3$. This change has been recentlyproposed by the US Environmental Protection Agency (EPA). Our goal is toestimate the reduction in deaths that would result from this anticipatedrevision using data consisting of 68 million individuals across the U.S.

Causal Estimation of Exposure Shifts with Neural Networks