| Estimating individual treatment effect: generalization bounds and algorithms U Shalit, F Johansson, D Sontag International Conference on Machine Learning, 3076-3085, 2017 | 1253 | 2017 |
| Large scale online learning of image similarity through ranking G Chechik, V Sharma, U Shalit, S Bengio The Journal of Machine Learning Research 11, 1109-1135, 2010 | 1052 | 2010 |
| Learning representations for counterfactual inference F Johansson, U Shalit, D Sontag International Conference on Machine Learning, 3020-3029, 2016 | 915 | 2016 |
| Causal effect inference with deep latent-variable models C Louizos, U Shalit, JM Mooij, D Sontag, R Zemel, M Welling Advances in Neural Information Processing Systems, 6446-6456, 2017 | 908 | 2017 |
| Structured inference networks for nonlinear state space models R Krishnan, U Shalit, D Sontag Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 566 | 2017 |
| Deep Kalman Filters RG Krishnan, U Shalit, D Sontag arXiv preprint arXiv:1511.05121, 2015 | 459 | 2015 |
| Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition V Dorie, J Hill, U Shalit, M Scott, D Cervone Statistical Science 34 (1), 43-68, 2019 | 393 | 2019 |
| COVID-19 dynamics after a national immunization program in Israel H Rossman, S Shilo, T Meir, M Gorfine, U Shalit, E Segal Nature Medicine, 1-7, 2021 | 270* | 2021 |
| Explaining classifiers with causal concept effect (cace) Y Goyal, A Feder, U Shalit, B Kim arXiv preprint arXiv:1907.07165, 2019 | 190 | 2019 |
| CausaLM: Causal model explanation through counterfactual language models A Feder, N Oved, U Shalit, R Reichart Computational Linguistics, 1-52, 2020 | 180 | 2020 |
| On calibration and out-of-domain generalization Y Wald, A Feder, D Greenfeld, U Shalit Advances in neural information processing systems 34, 2215-2227, 2021 | 164 | 2021 |
| Developing a COVID-19 mortality risk prediction model when individual-level data are not available N Barda, D Riesel, A Akriv, J Levy, U Finkel, G Yona, D Greenfeld, ... Nature Communications 11 (1), 1-9, 2020 | 154 | 2020 |
| Removing Hidden Confounding by Experimental Grounding N Kallus, AM Puli, U Shalit Advances in Neural Information Processing Systems, 10888-10897, 2018 | 153 | 2018 |
| A causal view of compositional zero-shot recognition Y Atzmon, F Kreuk, U Shalit, G Chechik Advances in Neural Information Processing Systems 33, 2020 | 152 | 2020 |
| An online algorithm for large scale image similarity learning G Chechik, V Sharma, U Shalit, S Bengio Advances in Neural Information Processing Systems 21, 306-314, 2009 | 148 | 2009 |
| Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review B Kenner, ST Chari, D Kelsen, DS Klimstra, SJ Pandol, M Rosenthal, ... Pancreas 50 (3), 251-279, 2021 | 146 | 2021 |
| Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects FD Johansson, U Shalit, N Kallus, D Sontag Journal of Machine Learning Research 23 (166), 1-50, 2022 | 127 | 2022 |
| Hospital load and increased COVID-19 related mortality in Israel H Rossman, T Meir, J Somer, S Shilo, R Gutman, AB Arie, E Segal, ... Nature Communications 12 (1), 1-7, 2021 | 102 | 2021 |
| Socioeconomic disparities and COVID-19 vaccination acceptance: a nationwide ecologic study G Caspi, A Dayan, Y Eshal, S Liverant-Taub, G Twig, U Shalit, Y Lewis, ... Clinical Microbiology and Infection 27 (10), 1502-1506, 2021 | 99 | 2021 |
| Learning Weighted Representations for Generalization Across Designs FD Johansson, N Kallus, U Shalit, D Sontag arXiv preprint arXiv:1802.08598, 2018 | 94 | 2018 |
David SontagProfessor, Massachusetts Institute of Technology確認したメール アドレス: csail.mit.edu