A Model of Multiple Hypothesis Testing

This paper develops an economic framework for determining when and how much multiple hypothesis testing (MHT) adjustment is warranted in research settings. The research question is: under what conditions do MHT adjustments arise as an optimal solution to incentive misalignment between a researcher and a mechanism designer (social planner)?

The model is a two-stage game. In the first stage, a benevolent social planner commits to a hypothesis testing protocol. In the second stage, a researcher decides whether to conduct a pre-specified experiment based on private costs and benefits. The planner’s utility function combines an ambiguity-averse (maximin) component—limiting harm from mistaken conclusions—with an expected-utility component capturing the generic benefits of research production. The framework focuses on multiplicity arising from testing multiple treatments or estimating effects within multiple subpopulations; multiple outcomes are treated as an economically distinct case covered in a companion paper.

Debiasing and T-Tests for Synthetic Control Inference on Average Causal Effects

Chernozhukov, Wüthrich, and Zhu propose a debiased synthetic control (SC) estimator and an accompanying self-normalized t-test for making inferences on the average treatment effect on the treated (ATT) in aggregate panel data settings with one treated unit. The inferential target is the time-averaged treatment effect τ = (1/T1) Σ_{t=T0+1}^{T} (Y0t(1) − Y0t(0)), a one-number summary of the overall causal impact that admits standard-form confidence intervals, in contrast to per-period effects (which cannot be consistently estimated with one treated unit) and sharp null hypotheses (which do not inform effect magnitude).

Kaspar Wüthrich | Macro Paper Warehouse

A Model of Multiple Hypothesis Testing

Debiasing and T-Tests for Synthetic Control Inference on Average Causal Effects