Meta-Learners for Partially-Identified Treatment Effects Across Multiple
Environments
Meta-Learners for Partially-Identified Treatment Effects Across Multiple
Environments
Estimating the conditional average treatment effect (CATE) from observational data is relevant for many applications such as personalized medicine. Here, we focus on the widespread setting where the observational data come from multiple environments, such as different hospitals, physicians, or countries. Furthermore, we allow for violations of standard causal assumptions, …