Published or forthcoming papers
- (2023) “Why Randomize? Minimax Optimality under Permutation Invariance,” Journal of Econometrics 232(2), 565–575. doi
- (2022) “Optimality of Matched-Pair Designs in Randomized Controlled Trials,” American Economic Review 112(12), 3911–3940. doi supplement
- (2022) “Inference in Experiments with Matched Pairs” (with J. P. Romano and A. M. Shaikh), Journal of the American Statistical Association 117(540), 1726–1737. doi supplement code
- (2022) “A Two-Step Method for Testing Many Moment Inequalities” (with A. Santos and A. M. Shaikh), Journal of Business and Economic Statistics 40(3), 1070–1080. doi code
- (2021) “Inference for Support Vector Regression under $\ell_1$ Regularization” (with H. Ho, G. A. Pouliot, and J. K. C. Shea), AEA Papers and Proceedings 111, 611–615. doi
- (2023) “On the Efficiency of Finely Stratified Experiments” (with J. Liu, A. M. Shaikh, and M. Tabord-Meehan), working paper. doi
- (2023) “Inference in Experiments with Matched Pairs and Imperfect Compliance” (with H. Guo, A. M. Shaikh, and M. Tabord-Meehan), working paper. doi
- (2023) “Inference in Cluster Randomized Trials with Matched Pairs” (with J. Liu, A. M. Shaikh, and M. Tabord-Meehan), working paper. doi
- (2023) “Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition” (with M. H. Hsieh, J. Liu, and M. Tabord-Meehan), revision requested by the Journal of Applied Econometrics. doi code
- (2023) “Inference for Matched Tuples and Fully Blocked Factorial Designs” (with J. Liu and M. Tabord-Meehan), revision requested by Quantitative Economics. doi code
- (2023) “Covariate Adjustment in Experiments with Matched Pairs” (with L. Jiang, J. P. Romano, A. M. Shaikh, and Y. Zhang), revision requested by the Journal of Econometrics. doi
- (2022) “On Testing Systems of Linear Inequalities with Known Coefficients” (with A. Santos and A. M. Shaikh), working paper.
(2022) “Partial Identification of Treatment Effect Rankings with Instrumental Variables” (with A. M. Shaikh and E. J. Vytlacil), working paper (draft coming soon). abstract slides
This paper develops partial-identification and inference for treatment effect parameters and the rankings of treatments in an instrumental variable framework while imposing alternative monotonicity restrictions. In particular, we consider a discrete, multi-valued treatment, a binary outcome, and a discrete, possibly multi-valued instrument. We use a linear programming formulation to present a flexible framework and to develop general results for characterizing the testable restrictions and the sharp identification of treatment effect parameters and the rankings of treatments in terms of these parameters that follow from imposing instrument exogeneity while additionally imposing alternative monotonicity restrictions on how the treatments depend on the instruments and how the outcomes depend on the treatments. Our results nest both ordered and unordered treatments. We further characterize leading special cases of our general analysis. We develop methods for simultaneous inference about the consistency of the observed data with our restrictions and the treatment effect ranking when the distribution of the observed data is consistent with our restrictions. We illustrate our methodology with empirical applications to the encouragement design of Behaghel, Crepon and Gurgand (2014) investigating the effects of public vs private job search assistance; the RCTs with one-sided non-compliance of Angrist, Lang and Oreopoulos (2009) investigating the effects of alternative strategies on academic performance of college students and of Blattman, Jamison, and Sheridan (2017) investigating the effects cash incentives and therapy on reducing crime in Liberia; and the RCT with close substitutes of Kline and Walters (2016) investigating the effects of alternative early childhood programs.