I am currently working on problems in multiple testing - more projects in progress
Working Papers:
Inference on Multiple Winners with Applications to Economic Mobility [slides](with Azeem Shaikh)
submitted
While policymakers and researchers are often concerned with conducting inference based on a data-dependent selection, a strictly larger class of inference problems arises when considering multiple data-dependent selections. Given this, we revisit the inference on winners problem from Andrews, Kitagawa, and McCloskey (2023), studying an empirically relevant generalization which we dub the inference on multiple winners problem. In this setting, we encounter both selective and multiple testing problems, making existing approaches either not applicable or too conservative. Instead, we propose a novel, two-step approach to the inference on multiple winners problem, with the first step modeling the selection of winners, and the second step using this model to conduct inference only on the set of likely winners. Our two-step approach reduces over-coverage error by up to 92% and confidence-set volume by up to 35%. Finally, we apply our two-step approach to revisit the winner's curse in the creating moves to opportunity (CMTO) program, and to study external validity issues in the microcredit literature. In the CMTO application, we find that, after correcting for the inference on multiple winners problem, we fail to reject the possibility of null effects in the majority of census tracts selected by the CMTO program. We also find that our novel two-step approach provides simultaneous confidence sets that are 5% shorter, on each axis, than existing approaches.
Works in Progress:
Uniform Inference for Empirical Bayes Estimands with Applications to Ranking and Selection