Daniel Malinsky

21 papers A* 1A 6Journal 11Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. J. Am. Medical Informatics Assoc. Shalmali Joshi, Iñigo Urteaga, Wouter A. C. van Amsterdam, George Hripcsak, Pierre A. Elias, Benjamin R. C. Amor, Noémie Elhadad, James C. Fackler, Mark P. Sendak, Jenna Wiens, Kaivalya Deshpande, Yoav Wald, Madalina Fiterau, Zachary C. Lipton, Daniel Malinsky, Madhur Nayan, Hongseok Namkoong, Soojin Park, Julia E. Vogt, Rajesh Ranganath
2025	J	`jnl`	Bridging Prediction and Intervention Problems in Social Systems. CoRR Lydia T. Liu, Inioluwa Deborah Raji, Angela Zhou, Luke Guerdan, Jessica Hullman, Daniel Malinsky, Bryan Wilder, Simone Zhang, Hammaad Adam, Amanda Coston, Benjamin Laufer, Ezinne Nwankwo, Michael Zanger-Tishler, Eli Ben-Michael, Solon Barocas, Avi Feller, Marissa Gerchick, Talia Gillis, Shion Guha, Daniel E. Ho, Lily Hu, Kosuke Imai, Sayash Kapoor, Joshua Loftus, Razieh Nabi, Arvind Narayanan, Ben Recht, Juan Carlos Perdomo, Matthew J. Salganik, Mark P. Sendak, Alexander Tolbert, Berk Ustun, Suresh Venkatasubramanian, Angelina Wang, Ashia Wilson
2025	J	`jnl`	Corrigendum to "Estimating bounds on causal effects in high-dimensional and possibly confounded systems" [Int. J. Approx. Reason. 88 (2017) 371-384]. Int. J. Approx. Reason. Daniel Malinsky, Peter Spirtes
2025	J	`jnl`	Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning. Trans. Mach. Learn. Res. Numair Sani, Daniel Malinsky, Ilya Shpitser
2023	A	`conf`	Causal inference with outcome-dependent missingness and self-censoring. UAI Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya
2022	—	`conf`	Optimal Training of Fair Predictive Models. CLeaR Razieh Nabi, Daniel Malinsky, Ilya Shpitser
2021	A	`conf`	Differentiable Causal Discovery Under Unmeasured Confounding. AISTATS Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser
2020	J	`jnl`	Differentiable Causal Discovery Under Unmeasured Confounding. CoRR Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser
2020	J	`jnl`	Explaining The Behavior Of Black-Box Prediction Algorithms With Causal Learning. CoRR Numair Sani, Daniel Malinsky, Ilya Shpitser
2019	A	`conf`	A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects. AISTATS Daniel Malinsky, Ilya Shpitser, Thomas S. Richardson
2019	A	`conf`	Causal Inference Under Interference And Network Uncertainty. UAI Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
2019	J	`jnl`	Causal Inference Under Interference And Network Uncertainty. CoRR Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser
2019	A*	`conf`	Learning Optimal Fair Policies. ICML Razieh Nabi, Daniel Malinsky, Ilya Shpitser
2019	A	`conf`	Learning the Structure of a Nonstationary Vector Autoregression. AISTATS Daniel Malinsky, Peter Spirtes
2019	J	`jnl`	Optimal Training of Fair Predictive Models. CoRR Razieh Nabi, Daniel Malinsky, Ilya Shpitser
2018	A	`conf`	Causal Learning for Partially Observed Stochastic Dynamical Systems. UAI Søren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen
2018	—	`conf`	Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. CD@KDD Daniel Malinsky, Peter Spirtes
2018	J	`jnl`	Intervening on structure. Synth. Daniel Malinsky
2018	J	`jnl`	Learning Optimal Fair Policies. CoRR Razieh Nabi, Daniel Malinsky, Ilya Shpitser
2017	J	`jnl`	Estimating bounds on causal effects in high-dimensional and possibly confounded systems. Int. J. Approx. Reason. Daniel Malinsky, Peter Spirtes
2016	—	`conf`	Estimating Causal Effects with Ancestral Graph Markov Models. Probabilistic Graphical Models Daniel Malinsky, Peter Spirtes