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arXiv · 2608.00404

Debiased inference for proximal dose-response function

Daeyoung Ham·Sihan Wu·Yifan Cui
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Abstract

In this paper, we study nonparametric inference for the causal dose-response curve of a continuous-treatment under unmeasured confounding by leveraging treatment- and outcome-inducing confounding proxies. To estimate the curve, we introduce a novel proximal doubly robust pseudo-outcome whose conditional mean given treatment equals the dose-response curve whenever either bridge function is correctly specified, thereby addressing a key gap in proximal causal inference for continuous-treatments. Furthermore, we derive an influence function for its smoothed causal estimand, and construct a cross-fitted debiased local-linear estimator with a proper local-quadratic bias correction. We establish pointwise and finite-dimensional asymptotic normality and a uniform Gaussian approximation over compact treatment intervals. Both smoothing bandwidths may have the mean-squared-error-optimal order without undersmoothing, while cross-fitting accommodates flexible bridge estimators under a product convergence rate conditions without fitted-class entropy restrictions. We also develop practical bandwidth selectors, pointwise confidence intervals, and simultaneous confidence bands. Extensive simulations and a data analysis highlight the practical performance of the proposed method under latent confounding and multiple proxies.

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Daeyoung Ham, Sihan Wu, Yifan Cui. 2026-08-01. Debiased inference for proximal dose-response function. https://arxiv.org/abs/2608.00404

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