9 papers
Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression
Masahiro Kato
This study investigates semiparametric efficient estimation of causal and structural parameters in a semi-supervised setting. In our setting, unlabeled auxiliary regressors are ava…
Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
Masahiro Kato
This position paper argues that, in debiased machine learning, balancing functions should be derived from the Neyman orthogonal score, not chosen only as functions of covariates. C…
Sequential Audit Sampling for Finite Populations with Exact and Simulation-based Guarantee
Masahiro Kato, Kei Nakagawa
Financial statement auditors use a risk-based approach to evidence collection to obtain reasonable assurance. When an initial sample does not support a conclusion, they may inspect…
Conformal Prediction for Nonparametric Instrumental Regression
Masahiro Kato
We propose a method for constructing distribution-free prediction intervals in nonparametric instrumental variable regression (NPIV), with finite-sample coverage guarantees. Buildi…
General Bayesian Policy Learning
Masahiro Kato
This study proposes a General Bayes framework for policy learning. We consider decision problems in which a decision-maker chooses an action from a given set to maximize expected w…
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…
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…
Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
Masahiro Kato
This study investigates treatment effect estimation in the semi-supervised setting, also can be interpreted as prediction-powered inference. In our setting, we can use not only the…
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…