| Machine learning: an applied econometric approach S Mullainathan, J Spiess Journal of Economic Perspectives 31 (2), 87-106, 2017 | 2223 | 2017 |
| Revisiting event-study designs: robust and efficient estimation K Borusyak, X Jaravel, J Spiess Review of Economic Studies, rdae007, 2024 | 1588 | 2024 |
| Robust post-matching inference A Abadie, J Spiess Journal of the American Statistical Association 117 (538), 983-995, 2022 | 195 | 2022 |
| Big data and discrimination TB Gillis, JL Spiess The University of Chicago Law Review 86 (2), 459-488, 2019 | 191 | 2019 |
| Megastudies improve the impact of applied behavioural science KL Milkman, D Gromet, H Ho, JS Kay, TW Lee, P Pandiloski, Y Park, A Rai, ... Nature 600 (7889), 478-483, 2021 | 180 | 2021 |
| A 680,000-person megastudy of nudges to encourage vaccination in pharmacies KL Milkman, L Gandhi, MS Patel, HN Graci, DM Gromet, H Ho, JS Kay, ... Proceedings of the National Academy of Sciences 119 (6), e2115126119, 2022 | 121 | 2022 |
| Blueprint for a cyclical shock insurance in the euro area H Enderlein, J Spiess, L Guttenberg, A Vitorino Notre Europe, 2013 | 100 | 2013 |
| Machine-learning tests for effects on multiple outcomes J Ludwig, S Mullainathan, J Spiess arXiv preprint arXiv:1707.01473, 2017 | 34 | 2017 |
| Making One Size Fit All: Designing at Cyclical Adjustment Insurance Fund for the Eurozone H Enderlein, L Guttenberg, J Spiess Jacques Delors Institut, 2013 | 32 | 2013 |
| Optimal estimation when researcher and social preferences are misaligned J Spiess Unpublished Manuscript, 2018 | 31 | 2018 |
| A design-based perspective on synthetic control methods L Bottmer, GW Imbens, J Spiess, M Warnick Journal of Business & Economic Statistics 42 (2), 762-773, 2024 | 22 | 2024 |
| Augmenting pre-analysis plans with machine learning J Ludwig, S Mullainathan, J Spiess Aea papers and proceedings 109, 71-76, 2019 | 18 | 2019 |
| Synthetic design: An optimization approach to experimental design with synthetic controls N Doudchenko, K Khosravi, J Pouget-Abadie, S Lahaie, M Lubin, ... Advances in Neural Information Processing Systems 34, 8691-8701, 2021 | 16 | 2021 |
| On the fairness of machine-assisted human decisions T Gillis, B McLaughlin, J Spiess arXiv preprint arXiv:2110.15310, 2021 | 15 | 2021 |
| Unpacking the black box: Regulating algorithmic decisions L Blattner, S Nelson, J Spiess arXiv preprint arXiv:2110.03443, 2021 | 15 | 2021 |
| Improving inference from simple instruments through compliance estimation S Coussens, J Spiess arXiv preprint arXiv:2108.03726, 2021 | 14 | 2021 |
| Algorithmic assistance with recommendation-dependent preferences B McLaughlin, J Spiess arXiv preprint arXiv:2208.07626, 2022 | 9 | 2022 |
| Machine learning who to nudge: causal vs predictive targeting in a field experiment on student financial aid renewal S Athey, N Keleher, J Spiess arXiv preprint arXiv:2310.08672, 2023 | 8 | 2023 |
| Machine Learning Explainability and Fairness: Insights from Consumer Lending L Blattner, J Spiess FinRegLab Empirical White Paper, 2022 | 7* | 2022 |
| Double and single descent in causal inference with an application to high-dimensional synthetic control J Spiess, G Imbens, A Venugopal Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
Sendhil MullainathanUniversity of Chicago在 chicagobooth.edu 的电子邮件经过验证
