**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]