@inproceedings{
tripuraneni2023metaanalysis,
title={Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data},
author={Nilesh Tripuraneni and Dominique Perrault-Joncas and Dhruv Madeka and Dean Foster and Michael Jordan},
booktitle={NeurIPS 2023 Workshop Heavy Tails in Machine Learning},
year={2023},
url={https://openreview.net/forum?id=rWptAXSkIC}
}
A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of ground truth (or validation set) to test their performance. In this paper, we propose a novel cross-validation-like methodology to address this challenge. The key insight of our procedure is that the noisy (but unbiased) difference-of-means estimate can be used as a ground truth ``label" on a portion of the RCT, to test the performance of an estimator trained on the other portion. We combine this insight with an aggregation scheme, which borrows statistical strength across a large collection of RCTs, to present an end-to-end methodology for judging an estimator's ability to recover the underlying treatment effect. We evaluate our methodology across 699 RCTs implemented in the Amazon supply chain. In this heavy-tailed setting, our methodology suggests that procedures that aggressively downweight or truncate large values, while introducing bias, lower the variance enough to ensure that the treatment effect is more accurately estimated.
Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data
Nilesh Tripuraneni Dominique Perrault-Joncas Dhruv Madeka Dean Foster Michael Jordan