**Publications**

::: Causal Inference, Machine Learning, & Statistical Methodology :::

D. Malinsky, I. Shpitser, and E. J. Tchetgen Tchetgen (2019) “

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

J. D. Ramsey, D. Malinsky, and K. V. Bui (2019) “

**algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD**,” submitted. [TETRAD code]

R. Nabi, D. Malinsky, and I. Shpitser (2019) “

**Optimal Training of Fair Predictive Models**,” submitted.

R. Bhattacharya, D. Malinsky, and I. Shpitser (2019) “

**Causal Inference Under Interference and Network Uncertainty**,”

*Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence*(UAI). [supp]

R. Nabi, D. Malinsky, and I. Shpitser (2019) “

**Learning Optimal Fair Policies**,”

*Proceedings of the 36th International Conference on Machine Learning*(ICML). [supp]

D. Malinsky, I. Shpitser, and T. S. Richardson (2019) “

**A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects**,”

*Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics*(AISTATS). [supp]

D. Malinsky and P. Spirtes (2019) “

**Learning the Structure of a Nonstationary Vector Autoregression**,”

*Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics*(AISTATS). [supp]

S. W. Mogensen, D. Malinsky, and N. R. Hansen (2018) “

**Causal Learning for Partially Observed Stochastic Dynamical Systems**,”

*Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence*(UAI). [supp]

D. Malinsky and P. Spirtes (2018) “

**Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding**,”

*Proceedings of the 2018 ACM SIGKDD Workshop on Causal Discovery*(KDD-CD). [TETRAD code]

D. Malinsky and D. Danks (2018) “

**Causal Discovery Algorithms: A Practical Guide**,”

*Philosophy Compass*13: e12470.

D. Malinsky and P. Spirtes (2017) “

**Estimating Bounds on Causal Effects in High-Dimensional and Possibly Confounded Systems**,”

*International Journal of Approximate Reasoning*88: 371-384. [R code]

D. Malinsky and P. Spirtes (2016) “

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

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

*.*

::: Philosophy of Science :::

D. Malinsky (2018) “

**Intervening on Structure**,”

*Synthese*135(5): 2295-2312.

L. K. Bright, D. Malinsky, and M. Thompson (2016) “

**Causally Interpreting Intersectionality Theory**,”

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

D. Malinsky (2015) “

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

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

::: Other :::

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 (2011) “

**Reconstruction and Identification Efficiency of Inclusive Isolated Photons**,” ATLAS Collaboration Note ATL-PHYS-INT-2011-014, CERN, Geneva.

**Select Presentations**

“Fairness by causal mediation analysis: criteria, algorithms, and open problems” (*invited talk)

- Johns Hopkins Behavioral Science Forum on Artificial Intelligence (Baltimore, USA), September 27, 2019.

“Learning optimal fair policies”

- 10th Workshop in Decisions, Games, & Logic: Ethics, Statistics, and Fair AI (Pasadena, USA), June 10, 2019.

“A potential outcomes calculus for identifying conditional path-specific effects”

- Atlantic Causal Inference Conference (Montreal, CA), May 23, 2019.

“A primer on causal structure learning with graphical models” (*invited talk)

- Division of General Medicine, Columbia University Medical Center (New York, USA), Feb 25, 2019.

“Causal structure learning from multivariate time series in settings with unmeasured confounding”

- KDD Workshop on Causal Discovery (London, UK), Aug 20, 2018.

“Causal structure learning from partially observed and nonstationary multivariate time series”

- Atlantic Causal Inference Conference (Pittsburgh, USA), May 21, 2018.

“Learning the structure of causal graphical models from observational data” (*invited talk)

- Department of Biostatistics, Harvard School of Public Health (Boston, USA), April 3, 2018.

“Learning ancestral graph Markov models from multivariate time series” (*invited talk)

- Seminar in Applied Mathematics and Statistics, University of Copenhagen (Copenhagen, Denmark), September 22, 2017.

“Graphical structure learning and data-driven causal inference for policy applications” (*invited talk)

- University of California, Riverside (Riverside, USA), April 20th, 2017.

“Learning causal models from time series data with latent variables” (*invited talk)

- 9th International Conference of the ERCIM WG on Computational and Methodological Statistics (Seville, Spain), December 9-11, 2016.

“Estimating causal effects with graphical models in systems with latent confounding” (*invited talk)

- Machine Learning Lunch Seminar at Carnegie Mellon (Pittsburgh, USA), October 3, 2016.

“Estimating causal effects with ancestral graph Markov models”

- Eighth International Conference on Probabilistic Graphical Models (Lugano, Switzerland), September 6-9, 2016

“Decision making under causal uncertainty”

- Explanation, Normativity, and Uncertainty in Economic Modelling at the London School of Economics (London, UK), March 16-17, 2016.
- Munich-Sydney-Tilburg Conference on Evidence, Inference, and Risk (Munich, Germany), March 31-April 2, 2016.

“Using graphical models for data-driven estimates of causal effects”

- XII Conference of the International Network for Economic Method (Cape Town, South Africa), November 19-22, 2015.

“Methods for determining photon efficiency systematics” (with Valeria Perez Reale)

- eGamma Group at CERN (Geneva, Switzerland), August 11, 2010.