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Journal Articles
- 2018
[j2]Daniel Malinsky
:
Intervening on structure. Synth. 195(5): 2295-2312 (2018)- 2017
- [j1]Daniel Malinsky, Peter Spirtes:
Estimating bounds on causal effects in high-dimensional and possibly confounded systems. Int. J. Approx. Reason. 88: 371-384 (2017)
Conference and Workshop Papers
- 2023
- [c10]Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya:
Causal inference with outcome-dependent missingness and self-censoring. UAI 2023: 358-368 - 2022
- [c9]Razieh Nabi, Daniel Malinsky, Ilya Shpitser:
Optimal Training of Fair Predictive Models. CLeaR 2022: 594-617 - 2021
- [c8]Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser:
Differentiable Causal Discovery Under Unmeasured Confounding. AISTATS 2021: 2314-2322 - 2019
- [c7]Daniel Malinsky, Peter Spirtes:
Learning the Structure of a Nonstationary Vector Autoregression. AISTATS 2019: 2986-2994 - [c6]Daniel Malinsky, Ilya Shpitser, Thomas S. Richardson:
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects. AISTATS 2019: 3080-3088 - [c5]Razieh Nabi, Daniel Malinsky, Ilya Shpitser:
Learning Optimal Fair Policies. ICML 2019: 4674-4682 - [c4]Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser:
Causal Inference Under Interference And Network Uncertainty. UAI 2019: 1028-1038 - 2018
- [c3]Daniel Malinsky, Peter Spirtes:
Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. CD@KDD 2018: 23-47 - [c2]Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen:
Causal Learning for Partially Observed Stochastic Dynamical Systems. UAI 2018: 350-360 - 2016
- [c1]Daniel Malinsky, Peter Spirtes:
Estimating Causal Effects with Ancestral Graph Markov Models. Probabilistic Graphical Models 2016: 299-309
Informal and Other Publications
- 2020
- [i5]Numair Sani, Daniel Malinsky, Ilya Shpitser:
Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning. CoRR abs/2006.02482 (2020) - [i4]Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser:
Differentiable Causal Discovery Under Unmeasured Confounding. CoRR abs/2010.06978 (2020) - 2019
- [i3]Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser:
Causal Inference Under Interference And Network Uncertainty. CoRR abs/1907.00221 (2019) - [i2]Razieh Nabi, Daniel Malinsky, Ilya Shpitser:
Optimal Training of Fair Predictive Models. CoRR abs/1910.04109 (2019) - 2018
- [i1]Razieh Nabi, Daniel Malinsky, Ilya Shpitser:
Learning Optimal Fair Policies. CoRR abs/1809.02244 (2018)
Coauthor Index
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