3 papers
cs.LG2026
Computational Identifiability
Lucius E. J. Bynum, Rajesh Ranganath, Kyunghyun Cho
Identification conditions describe the computability of a target query or parameter of interest as a function of the type and amount of information available. In causal identificat…
cs.LG2026
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
Donna Tjandra, Trenton Chang, Sonali Parbhoo +8
Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal…
cs.LG2025
Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do
Yoav Wald, Mark Goldstein, Yonathan Efroni +2
Problems in fields such as healthcare, robotics, and finance requires reasoning about the value both of what decision or action to take and when to take it. The prevailing hope is…