| Identification and estimation of causal effects defined by shift interventions N Sani, J Lee, I Shpitser Conference on Uncertainty in Artificial Intelligence, 949-958, 2020 | 13 | 2020 |
| Causal determinants of postoperative length of stay in cardiac surgery using causal graphical learning JJR Lee, R Srinivasan, CS Ong, D Alejo, S Schena, I Shpitser, ... The Journal of thoracic and cardiovascular surgery 166 (5), e446-e462, 2023 | 10 | 2023 |
| Identification methods with arbitrary interventional distributions as inputs JJR Lee, I Shpitser arXiv preprint arXiv:2004.01157, 2020 | 9 | 2020 |
| Ananke: A python package for causal inference using graphical models JJR Lee, R Bhattacharya, R Nabi, I Shpitser arXiv preprint arXiv:2301.11477, 2023 | 6 | 2023 |
| Path dependent structural equation models R Srinivasan, JJR Lee, R Bhattacharya, I Shpitser Uncertainty in Artificial Intelligence, 161-171, 2021 | 6 | 2021 |
| A semiparametric approach to interpretable machine learning N Sani, J Lee, R Nabi, I Shpitser arXiv preprint arXiv:2006.04732, 2020 | 5 | 2020 |
| Off-Policy Evaluation in Embedded Spaces JJR Lee, D Arbour, G Theocharous arXiv preprint arXiv:2203.02807, 2022 | 3 | 2022 |
| Markov-restricted analysis of randomized trials with non-monotone missing binary outcomes: Sensitivity analysis and identification results DO Scharfstein, JJR Lee, A McDermott, A Campbell, E Nunes, ... arXiv e-prints, arXiv: 2105.08868, 2021 | 3 | 2021 |
| A general identification algorithm for data fusion problems under systematic selection JJR Lee, AE Ghassami, I Shpitser arXiv preprint arXiv:2404.06602, 2024 | 2 | 2024 |
| METHODS FOR CAUSAL INFERENCE USING EXPERIMENTAL AND OBSERVATIONAL DATA JJR Lee Johns Hopkins University, 2024 | | 2024 |
| An Introduction to Causal Inference Methods for Observational Human-Robot Interaction Research JJR Lee, G Ajaykumar, I Shpitser, CM Huang arXiv preprint arXiv:2310.20468, 2023 | | 2023 |
| Computationally Efficient Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomes JJR Lee, I Shpitser, DO Scharfstein Atlantic Causal Inference Conference, 2019 | | 2019 |
| A Variational Bayes Approach to Clustered Latent Preference Models for Directed Network Data JJR Lee The Australian National University, 2016 | | 2016 |
Ilya ShpitserJohn C. Malone Associate Professor, Department of Computer Science, Johns Hopkins UniversityCorreu electrònic verificat a cs.jhu.edu
