6 citations · 11 across the 12 of their papers we have counts for
4 papers · 1 filter
Sharp Testable Implications of Encouragement Designs
Yuehao Bai, Shunzhuang Huang, Max Tabord-Meehan
This paper studies a potential outcome model with a continuous or discrete outcome, a discrete multi-valued treatment, and a discrete multi-valued instrument. We derive sharp, clos…
Testing the Fairness-Accuracy Improvability of Algorithms
Eric Auerbach, Annie Liang, Kyohei Okumura +1
Many organizations use algorithms that have a disparate impact, i.e., the benefits or harms of the algorithm fall disproportionately on certain social groups. Addressing an algorit…
A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances
Yuehao Bai, Azeem M. Shaikh, Max Tabord-Meehan
The past two decades have witnessed a surge of new research in the analysis of randomized experiments. The emergence of this literature may seem surprising given the widespread use…
Exposure effects are not automatically useful for policymaking
Eric Auerbach, Jonathan Auerbach, Max Tabord-Meehan
We thank Savje (2023) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists. In his article, Savje recommends misspecified ex…