3 papers
econ.EM2025
Causal-Policy Forest for End-to-End Policy Learning
Masahiro Kato
This study proposes an end-to-end algorithm for policy learning in causal inference. We observe data consisting of covariates, treatment assignments, and outcomes, where only the o…
stat.ML2025
Bridging the Gap between Empirical Welfare Maximization and Conditional Average Treatment Effect Estimation in Policy Learning
Masahiro Kato
The goal of policy learning is to train a policy function that recommends a treatment given covariates to maximize population welfare. There are two major approaches in policy lear…
econ.EM2025
Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
Masahiro Kato
This study considers the estimation of the direct bias-correction term for estimating the average treatment effect (ATE). Let be the observations, w…