Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference

Name: Semiparametric Double Reinforcement Learning with Applications to Long-Term Causal Inference
Uploaded: 2026-09-03T00:36:00.432117+00:00
Duration: 323 s
Description: The episode discusses a paper on Semiparametric Double Reinforcement Learning designed for long-term causal inference. The authors address practical hurdles like insufficient data overlap and high estimation instability. They propose a semiparametric framework using stable, superefficient estimators to model complex, long-term decision-making processes.

The episode discusses a paper on Semiparametric Double Reinforcement Learning designed for long-term causal inference. The authors address practical hurdles like insufficient data overlap and high estimation instability. They propose a semiparametric framework using stable, superefficient estimators to model complex, long-term decision-making processes.

Open the full episode