Ashesh Rambachan

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Sendhil MullainathanUniversity of ChicagoVerified email at chicagobooth.edu
Jens LudwigUniversity of ChicagoVerified email at uchicago.edu
Jonathan RothBrown UniversityVerified email at brown.edu
Jon KleinbergProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Neil ShephardFrank B. Baird Jr, Professor of Science, Dept of Economics & Dept of Statistics, Harvard UniversityVerified email at fas.harvard.edu
Amanda CostonMicrosoftVerified email at alumni.cmu.edu
Iavor BojinovAssistant Professor & Richard Hodgson Fellow, Harvard Business SchoolVerified email at hbs.edu
Alexandra ChouldechovaAssociate Professor of Statistics & Public Policy @ Heinz College, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Edward H. KennedyAssociate Professor of Statistics & Data Science, Carnegie Mellon UniversityVerified email at stat.cmu.edu

Ashesh Rambachan

Assistant Professor of Economics, Massachusetts Institute of Technology

Verified email at mit.edu - Homepage

Econometrics Causal Inference Algorithms


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A more credible approach to parallel trends A Rambachan, J Roth Review of Economic Studies 90 (5), 2555-2591, 2023	563*	2023
Algorithmic fairness J Kleinberg, J Ludwig, S Mullainathan, A Rambachan Aea papers and proceedings 108, 22-27, 2018	427	2018
An economic perspective on algorithmic fairness A Rambachan, J Kleinberg, J Ludwig, S Mullainathan AEA Papers and Proceedings 110, 91-95, 2020	73	2020
Panel experiments and dynamic causal effects: A finite population perspective I Bojinov, A Rambachan, N Shephard Quantitative Economics 12 (4), 1171-1196, 2021	65	2021
Characterizing fairness over the set of good models under selective labels A Coston, A Rambachan, A Chouldechova International Conference on Machine Learning, 2144-2155, 2021	65	2021
An economic approach to regulating algorithms A Rambachan, J Kleinberg, S Mullainathan, J Ludwig National Bureau of Economic Research, 2020	55	2020
Bias in, bias out? Evaluating the folk wisdom A Rambachan, J Roth arXiv preprint arXiv:1909.08518, 2019	47	2019
Identifying prediction mistakes in observational data A Rambachan Harvard University, 2021	43	2021
When do common time series estimands have nonparametric causal meaning? A Rambachan, N Shephard URL: https://scholar. harvard. edu/shephard/publications/nonparametric …, 2021	42*	2021
Design-based uncertainty for quasi-experiments A Rambachan, J Roth arXiv preprint arXiv:2008.00602, 2020	28	2020
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding A Rambachan, A Coston, E Kennedy arXiv preprint arXiv:2212.09844, 2022	11*	2022
From Predictive Algorithms to Automatic Generation of Anomalies S Mullainathan, A Rambachan Available at SSRN 4443738, 2023	2	2023
Causal Interpretation of Structural IV Estimands I Andrews, N Barahona, M Gentzkow, A Rambachan, JM Shapiro National Bureau of Economic Research, 2023	1	2023
The Unreasonable Effectiveness of Algorithms J Ludwig, S Mullainathan, A Rambachan National Bureau of Economic Research, 2024		2024

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