| Causally interpreting intersectionality theory LK Bright, D Malinsky, M Thompson Philosophy of Science 83 (1), 60-81, 2016 | 171 | 2016 |
| Causal discovery algorithms: A practical guide D Malinsky, D Danks Philosophy Compass 13 (1), e12470, 2018 | 119 | 2018 |
| Learning Optimal Fair Policies R Nabi, D Malinsky, I Shpitser Proceedings of the 36th International Conference on Machine Learning (ICML), 2019 | 100 | 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 | 97 | 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 | 58 | 2019 |
| Causal inference under interference and network uncertainty R Bhattacharya, D Malinsky, I Shpitser Uncertainty in Artificial Intelligence, 1028-1038, 2020 | 54 | 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 | 52 | 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 | 48 | 2018 |
| 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 | 41 | 2022 |
| 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 | 35 | 2017 |
| 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 | 28 | 2011 |
| Estimating causal effects with ancestral graph Markov models D Malinsky, P Spirtes Conference on Probabilistic Graphical Models, 299-309, 2016 | 25 | 2016 |
| Intervening on structure D Malinsky Synthese 195 (5), 2295-2312, 2018 | 22 | 2018 |
| 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 | 20 | 2022 |
| Learning the structure of a nonstationary vector autoregression D Malinsky, P Spirtes The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 20 | 2019 |
| Algcomparison: Comparing the performance of graphical structure learning algorithms with tetrad JD Ramsey, D Malinsky, KV Bui Journal of Machine Learning Research 21 (238), 1-6, 2020 | 18 | 2020 |
| Optimal training of fair predictive models R Nabi, D Malinsky, I Shpitser Conference on Causal Learning and Reasoning, 594-617, 2022 | 16 | 2022 |
| Explaining the behavior of black-box prediction algorithms with causal learning N Sani, D Malinsky, I Shpitser arXiv preprint arXiv:2006.02482, 2020 | 16 | 2020 |
| Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans ED Angelini, J Yang, PP Balte, EA Hoffman, AW Manichaikul, Y Sun, ... Thorax 78 (11), 1067-1079, 2023 | 9 | 2023 |
| Association of dysanapsis with mortality among older adults M Vameghestahbanati, C Sack, A Wysoczanski, EA Hoffman, E Angelini, ... European Respiratory Journal 61 (6), 2023 | 9 | 2023 |
Ilya ShpitserJohn C. Malone Associate Professor, Department of Computer Science, Johns Hopkins UniversityVerified email at cs.jhu.edu
