| Causally interpreting intersectionality theory LK Bright, D Malinsky, M Thompson Philosophy of Science 83 (1), 60-81, 2016 | 143 | 2016 |
| Causal discovery algorithms: A practical guide D Malinsky, D Danks Philosophy Compass 13 (1), e12470, 2018 | 91 | 2018 |
| Learning Optimal Fair Policies R Nabi, D Malinsky, I Shpitser Proceedings of the 36th International Conference on Machine Learning (ICML), 2019 | 77 | 2019 |
| Causal structure learning from multivariate time series in settings with unmeasured confounding D Malinsky, P Spirtes Proceedings of 2018 ACM SIGKDD workshop on causal discovery, 23-47, 2018 | 67 | 2018 |
| A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects D Malinsky, I Shpitser, T Richardson Proceedings of the 22nd International Conference on Artificial Intelligence …, 2019 | 44 | 2019 |
| Causal inference under interference and network uncertainty R Bhattacharya, D Malinsky, I Shpitser Uncertainty in Artificial Intelligence, 1028-1038, 2020 | 39 | 2020 |
| Differentiable causal discovery under unmeasured confounding R Bhattacharya, T Nagarajan, D Malinsky, I Shpitser International Conference on Artificial Intelligence and Statistics, 2314-2322, 2021 | 35 | 2021 |
| Causal Learning for Partially Observed Stochastic Dynamical Systems SW Mogensen, D Malinsky, NR Hansen Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence …, 2018 | 35 | 2018 |
| Reconstruction and identification efficiency of inclusive isolated photons L Carminati, M Delmastro, M Hance, MJ Belenguer, R Ishmukhametov, ... Technical Report ATL-PHYS-INT-2011-014, CERN, Geneva, 2011 | 29 | 2011 |
| Estimating bounds on causal effects in high-dimensional and possibly confounded systems D Malinsky, P Spirtes International Journal of Approximate Reasoning 88, 371-384, 2017 | 27 | 2017 |
| Semiparametric inference for nonmonotone missing-not-at-random data: the no self-censoring model D Malinsky, I Shpitser, EJ Tchetgen Tchetgen Journal of the American Statistical Association 117 (539), 1415-1423, 2022 | 25 | 2022 |
| algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD JD Ramsey, D Malinsky, KV Bui The Journal of Machine Learning Research 21 (1), 9649-9654, 2020 | 18 | 2020 |
| Estimating causal effects with ancestral graph Markov models D Malinsky, P Spirtes Conference on Probabilistic Graphical Models, 299-309, 2016 | 17 | 2016 |
| Intervening on structure D Malinsky Synthese 195 (5), 2295-2312, 2018 | 16 | 2018 |
| Learning the structure of a nonstationary vector autoregression D Malinsky, P Spirtes The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 14 | 2019 |
| Explaining the behavior of black-box prediction algorithms with causal learning N Sani, D Malinsky, I Shpitser arXiv preprint arXiv:2006.02482, 2020 | 11 | 2020 |
| Optimal training of fair predictive models R Nabi, D Malinsky, I Shpitser Conference on Causal Learning and Reasoning, 594-617, 2022 | 10 | 2022 |
| Multicenter study of racial and ethnic inequities in liver transplantation evaluation: Understanding mechanisms and identifying solutions AT Strauss, CN Sidoti, TS Purnell, HC Sung, JW Jackson, S Levin, ... Liver Transplantation 28 (12), 1841-1856, 2022 | 6 | 2022 |
| Disease-specific contribution of pulvinar dysfunction to impaired emotion recognition in schizophrenia A Martínez, RH Tobe, PA Gaspar, D Malinsky, EC Dias, P Sehatpour, ... Frontiers in Behavioral Neuroscience 15, 379, 2022 | 6 | 2022 |
| 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, 2022 | 2 | 2022 |
Ilya ShpitserJohn C. Malone Associate Professor, Department of Computer Science, Johns Hopkins UniversityVerified email at cs.jhu.edu
