6 papers
Causality Elicitation from Large Language Models
Takashi Kameyama, Masahiro Kato, Yasuko Hio +2
Large language models (LLMs) are trained on enormous amounts of data and encode knowledge in their parameters. We propose a pipeline to elicit causal relationships from LLMs. Speci…
genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
Masahiro Kato
Efficient estimation of causal and structural parameters can be automated using the Riesz representation theorem and debiased machine learning (DML). We present genriesz, an open-s…
Riesz Regression As Direct Density Ratio Estimation
Masahiro Kato
This study clarifies the relationship between Riesz regression [Chernozhukov et al., 2021] and density ratio estimation (DRE) in causal inference problems, such as average treatmen…
A Unified Theory for Causal Inference: Direct Debiased Machine Learning via Bregman-Riesz Regression
Masahiro Kato
This note introduces a unified theory for causal inference that integrates Riesz regression, covariate balancing, density-ratio estimation (DRE), targeted maximum likelihood estima…
Direct Debiased Machine Learning via Bregman Divergence Minimization
Masahiro Kato
We develop a direct debiased machine learning framework comprising Neyman targeted estimation and generalized Riesz regression. Our framework unifies Riesz regression for automatic…
Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regression
Masahiro Kato
This study proves that Nearest Neighbor (NN) matching can be interpreted as an instance of Riesz regression for automatic debiased machine learning. Lin et al. (2023) shows that NN…