**Publications**

__Causal Inference, Statistical Methodology, and Machine Learning__

**Differentiable Causal Structure Learning Under Unmeasured Confounding**

R. Bhattacharya, T. Nagarajan, D. Malinsky, and I. Shpitser

submitted, 2020.

[arXiv]

Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning

Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning

N. Sani, D. Malinsky, and I. Shpitser

submitted, 2020.

[arXiv]

**Semiparametric Inference for Non-monotone Missing-Not-at-Random Data: the No Self-Censoring Model**

D. Malinsky, I. Shpitser, and E. J. Tchetgen Tchetgen

submitted, 2019.

[arXiv]

**algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD**

J. D. Ramsey, D. Malinsky, and K. V. Bui

submitted, 2019.

[arXiv] [code]

**Optimal Training of Fair Predictive Models**

R. Nabi, D. Malinsky, and I. Shpitser

submitted, 2019.

[arXiv]

**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]

**Causal Discovery Algorithms: A Practical Guide**

D. Malinsky and D. Danks

*Philosophy Compass*13: e12470, 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]

**Estimating Causal Effects with Ancestral Graph Markov Models**

D. Malinsky and P. Spirtes

*Proceedings of the Eighth International Conference on Probabilistic Graphical Models*(PGM), 2016

*.*

[paper]

__Philosophy of Science__

**Intervening on Structure**

D. Malinsky

*Synthese*135(5): 2295-2312, 2018

[paper]

**Causally Interpreting Intersectionality Theory**

L. K. Bright, D. Malinsky, and M. Thompson

*Philosophy of Science*83(1): 60-81, 2016.

[paper]

**Hypothesis Testing, ‘Dutch Book’ Arguments, and Risk**

D. Malinsky

*Philosophy of Science*82(5): 917-929, 2015.

[paper]

__Other Publications__

**Reconstruction and Identification Efficiency of Inclusive Isolated Photons**

L. Carminati, M. Delmastro, M. Hance, M. Jimenez Belenguer, R. Ishmukhametov, Z. Liang, G. Marchiori, V. Perez Reale, D. Malinsky, M. Tripiana, and G. Unal

ATLAS Collaboration Note ATL-PHYS-INT-2011-014, CERN, Geneva, 2011.

[abstract]