Malinsky, Daniel

11 publications

TMLR 2025 Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning Numair Sani, Daniel Malinsky, Ilya Shpitser

UAI 2023 Causal Inference with Outcome-Dependent Missingness and Self-Censoring Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya

CLeaR 2022 Optimal Training of Fair Predictive Models Razieh Nabi, Daniel Malinsky, Ilya Shpitser

AISTATS 2021 Differentiable Causal Discovery Under Unmeasured Confounding Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser

MLOSS 2020 Algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui

AISTATS 2019 A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects Daniel Malinsky, Ilya Shpitser, Thomas Richardson

UAI 2019 Causal Inference Under Interference and Network Uncertainty Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser

ICML 2019 Learning Optimal Fair Policies Razieh Nabi, Daniel Malinsky, Ilya Shpitser

AISTATS 2019 Learning the Structure of a Nonstationary Vector Autoregression Daniel Malinsky, Peter Spirtes

UAI 2018 Causal Learning for Partially Observed Stochastic Dynamical Systems Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen

PGM 2016 Estimating Causal Effects with Ancestral Graph Markov Models Daniel Malinsky, Peter Spirtes