Author

D. Ham

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Preprint Aug 2026

Debiased inference for proximal dose-response function

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 trea...

D. Ham, Si-Han Wu, Yifan Cui · 0 citations

#artificial intelligence Preprint Sep 2026

Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation

Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality standard. Yet using AI judgments for formal statistical inference is fundamentally different from simply treating them as ground-truth labels: AI evaluations can be bias...

D. Ham, Xue-Jun Zhao, S. Jasin et al. · 0 citations

Oct 2022

Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Panel Experiments

Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. Beyond aiding managerial decision making, experiments mitigate risk by limiting the proportion of customers exposed to innovations. Because many experiments are conducted sequentially over time,...

D. Ham, Michael Lindon, M. Tingley et al. · Management Sciences · 0 citations