Published or forthcoming papers
- (2022) “Optimality of Matched-Pair Designs in Randomized Controlled Trials,” forthcoming in by the American Economic Review. doi supplement
- (2022) “Why Randomize? Minimax Optimality under Permutation Invariance,” forthcoming in the Journal of Econometrics. doi
- (2022) “Inference in Experiments with Matched Pairs” (with J. P. Romano and A. M. Shaikh), forthcoming in the Journal of the American Statistical Association. 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, no. 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
- (2022) “Revisiting the Analysis of Matched-Pair and Stratified Experimental Designs in the Presence of Attrition” (with M. H. Hsieh, J. Liu, and M. Tabord-Meehan), working paper. doi
- (2022) “Inference for Matched Tuples and Fully Blocked Factorial Designs” (with J. Liu and M. Tabord-Meehan), working paper. doi code
- (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.