3 papers
cs.LG2026
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
Seyedeh Baharan Khatami, Harsh Parikh, Haowei Chen +2
We address the challenge of inferring causal effects in social network data. This results in challenges due to interference -- where a unit's outcome is affected by neighbors' trea…
stat.ME2025
A Double Machine Learning Approach to Combining Experimental and Observational Data
Harsh Parikh, Marco Morucci, Vittorio Orlandi +3
Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational st…
stat.ME2025
Data Fusion for Partial Identification of Causal Effects
Quinn Lanners, Cynthia Rudin, Alexander Volfovsky +1
Data fusion techniques integrate information from heterogeneous data sources to improve learning, generalization, and decision making across data sciences. In causal inference, the…