For up-to-date publication list: [Google Scholar]
Selected publications
Post-selection Inference for Causal Effects After Causal Discovery
T-H. Chang, Z. Guo, and D. Malinsky
Submitted.
[arXiv]
A Cautious Approach to Constraint-Based Causal Model Selection
D. Malinsky
Submitted.
[arXiv]
Mediated Probabilities of Causation
M. Rubenstein, M. Cuellar, and D. Malinsky
Submitted.
[arXiv]
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning
N. Sani, D. Malinsky, and I. Shpitser
Submitted.
[arXiv]
Causal Inference with Outcome-Dependent Missingness and Self-Censoring
J. M. Chen, D. Malinsky, and R. Bhattacharya
Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
[paper]
Causal Determinants of Postoperative Length of Stay in Cardiac Surgery Using Causal Graphical Learning
J. J. R. Lee, R. Srinivasan, C. S. Ong, D. Alejo, S. Schena, I. Shpitser, M. Sussman, G. J. R. Whitman, and D. Malinsky
The Journal of Thoracic and Cardiovascular Surgery 166(5): e446-e462, 2023.
[paper]
Optimal Training of Fair Predictive Models
R. Nabi, D. Malinsky, and I. Shpitser
Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR), 2022.
[paper]
Semiparametric Inference for Non-monotone Missing-Not-at-Random Data: the No Self-Censoring Model
D. Malinsky, I. Shpitser, and E. J. Tchetgen Tchetgen
Journal of the American Statistical Association, 117(539): 1415-1423, 2022.
[paper] [arXiv] [erratum]
Differentiable Causal Structure Learning Under Unmeasured Confounding
R. Bhattacharya, T. Nagarajan, D. Malinsky, and I. Shpitser
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
[paper] [supplement]
algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
J. D. Ramsey, D. Malinsky, and K. V. Bui
Journal of Machine Learning Research 21(238): 1-6, 2020.
[paper] [code]
Causal Inference Under Interference and Network Uncertainty
R. Bhattacharya, D. Malinsky, and I. Shpitser
Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 2019.
[paper] [supplement]
Learning Optimal Fair Policies
R. Nabi, D. Malinsky, and I. Shpitser
Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.
[paper] [supplement]
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects
D. Malinsky, I. Shpitser, and T. S. Richardson
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
[paper] [supplement]
Learning the Structure of a Nonstationary Vector Autoregression
D. Malinsky and P. Spirtes
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
[paper] [supplement]
Causal Learning for Partially Observed Stochastic Dynamical Systems
S. W. Mogensen, D. Malinsky, and N. R. Hansen
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
[paper] [supplement]
Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding
D. Malinsky and P. Spirtes
Proceedings of the 2018 ACM SIGKDD Workshop on Causal Discovery (KDD-CD), 2018.
[paper] [code]
Intervening on Structure
D. Malinsky
Synthese 135(5): 2295-2312, 2018
[paper]
Estimating Bounds on Causal Effects in High-Dimensional and Possibly Confounded Systems
D. Malinsky and P. Spirtes
International Journal of Approximate Reasoning 88: 371-384, 2017.
[paper] [code]
Causally Interpreting Intersectionality Theory
L. K. Bright, D. Malinsky, and M. Thompson
Philosophy of Science 83(1): 60-81, 2016.
[paper]
Selected publications
Post-selection Inference for Causal Effects After Causal Discovery
T-H. Chang, Z. Guo, and D. Malinsky
Submitted.
[arXiv]
A Cautious Approach to Constraint-Based Causal Model Selection
D. Malinsky
Submitted.
[arXiv]
Mediated Probabilities of Causation
M. Rubenstein, M. Cuellar, and D. Malinsky
Submitted.
[arXiv]
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning
N. Sani, D. Malinsky, and I. Shpitser
Submitted.
[arXiv]
Causal Inference with Outcome-Dependent Missingness and Self-Censoring
J. M. Chen, D. Malinsky, and R. Bhattacharya
Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
[paper]
Causal Determinants of Postoperative Length of Stay in Cardiac Surgery Using Causal Graphical Learning
J. J. R. Lee, R. Srinivasan, C. S. Ong, D. Alejo, S. Schena, I. Shpitser, M. Sussman, G. J. R. Whitman, and D. Malinsky
The Journal of Thoracic and Cardiovascular Surgery 166(5): e446-e462, 2023.
[paper]
Optimal Training of Fair Predictive Models
R. Nabi, D. Malinsky, and I. Shpitser
Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR), 2022.
[paper]
Semiparametric Inference for Non-monotone Missing-Not-at-Random Data: the No Self-Censoring Model
D. Malinsky, I. Shpitser, and E. J. Tchetgen Tchetgen
Journal of the American Statistical Association, 117(539): 1415-1423, 2022.
[paper] [arXiv] [erratum]
Differentiable Causal Structure Learning Under Unmeasured Confounding
R. Bhattacharya, T. Nagarajan, D. Malinsky, and I. Shpitser
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
[paper] [supplement]
algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
J. D. Ramsey, D. Malinsky, and K. V. Bui
Journal of Machine Learning Research 21(238): 1-6, 2020.
[paper] [code]
Causal Inference Under Interference and Network Uncertainty
R. Bhattacharya, D. Malinsky, and I. Shpitser
Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 2019.
[paper] [supplement]
Learning Optimal Fair Policies
R. Nabi, D. Malinsky, and I. Shpitser
Proceedings of the 36th International Conference on Machine Learning (ICML), 2019.
[paper] [supplement]
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects
D. Malinsky, I. Shpitser, and T. S. Richardson
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
[paper] [supplement]
Learning the Structure of a Nonstationary Vector Autoregression
D. Malinsky and P. Spirtes
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
[paper] [supplement]
Causal Learning for Partially Observed Stochastic Dynamical Systems
S. W. Mogensen, D. Malinsky, and N. R. Hansen
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
[paper] [supplement]
Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding
D. Malinsky and P. Spirtes
Proceedings of the 2018 ACM SIGKDD Workshop on Causal Discovery (KDD-CD), 2018.
[paper] [code]
Intervening on Structure
D. Malinsky
Synthese 135(5): 2295-2312, 2018
[paper]
Estimating Bounds on Causal Effects in High-Dimensional and Possibly Confounded Systems
D. Malinsky and P. Spirtes
International Journal of Approximate Reasoning 88: 371-384, 2017.
[paper] [code]
Causally Interpreting Intersectionality Theory
L. K. Bright, D. Malinsky, and M. Thompson
Philosophy of Science 83(1): 60-81, 2016.
[paper]