| 2023 | UAI | Causal inference with outcome-dependent missingness and self-censoring. | Jacob M. Chen, Daniel Malinsky, Rohit Bhattacharya |
| 2021 | AISTATS | Differentiable Causal Discovery Under Unmeasured Confounding. | Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser |
| 2019 | AISTATS | A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects. | Daniel Malinsky, Ilya Shpitser, Thomas S. Richardson |
| 2019 | AISTATS | Learning the Structure of a Nonstationary Vector Autoregression. | Daniel Malinsky, Peter Spirtes |
| 2019 | ICML | Learning Optimal Fair Policies. | Razieh Nabi, Daniel Malinsky, Ilya Shpitser |
| 2019 | UAI | Causal Inference Under Interference And Network Uncertainty. | Rohit Bhattacharya, Daniel Malinsky, Ilya Shpitser |
| 2018 | KDD | Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding. | Daniel Malinsky, Peter Spirtes |
| 2018 | UAI | Causal Learning for Partially Observed Stochastic Dynamical Systems. | Sren Wengel Mogensen, Daniel Malinsky, Niels Richard Hansen |