Daniel Malinsky

Publication Activity (10 Years)

Years Active: 2016-2023
Publications (10 Years): 17

Top Topics

Partially Observed

Observational Data

Causal Inference

Top Venues

Int. J. Approx. Reason.

Publications

Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya
Causal inference with outcome-dependent missingness and self-censoring. UAI (2023)
Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Optimal Training of Fair Predictive Models. CLeaR (2022)
Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser
Differentiable Causal Discovery Under Unmeasured Confounding. AISTATS (2021)
Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser
Differentiable Causal Discovery Under Unmeasured Confounding. CoRR (2020)
Numair Sani, Daniel Malinsky, Ilya Shpitser
Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning. CoRR (2020)
Daniel Malinsky, Peter Spirtes
Learning the Structure of a Nonstationary Vector Autoregression. AISTATS (2019)
Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
Causal Inference Under Interference And Network Uncertainty. UAI (2019)
Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
Causal Inference Under Interference And Network Uncertainty. CoRR (2019)
Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Learning Optimal Fair Policies. ICML (2019)
Daniel Malinsky, Ilya Shpitser, Thomas S. Richardson
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects. AISTATS (2019)
Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Optimal Training of Fair Predictive Models. CoRR (2019)
Daniel Malinsky
Intervening on structure. Synth. 195 (5) (2018)
Daniel Malinsky, Peter Spirtes
Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. CD@KDD (2018)
Razieh Nabi, Daniel Malinsky, Ilya Shpitser
Learning Optimal Fair Policies. CoRR (2018)
Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen
Causal Learning for Partially Observed Stochastic Dynamical Systems. UAI (2018)
Daniel Malinsky, Peter Spirtes
Estimating bounds on causal effects in high-dimensional and possibly confounded systems. Int. J. Approx. Reason. 88 (2017)
Daniel Malinsky, Peter Spirtes
Estimating Causal Effects with Ancestral Graph Markov Models. Probabilistic Graphical Models (2016)