10 papers
Vector Search As Nearest Neighbor Matching: RAG-based Policy Learning in Causal Inference
Masahiro Kato, Taka Kato
We propose one-step and two-step methods for policy learning with retrieval-augmented generation (RAG). We formulate RAG-based action selection under the potential outcome framewor…
Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms?
Masahiro Kato, Taka Kato
Large language models (LLMs) are increasingly used to implement algorithms from research manuscripts, but papers often leave implementation choices implicit. This study examines ho…
AI Economist Agent: An Agentic Framework for Model-Grounded Economic Analysis with RAG, Knowledge Graphs, and Large Language Models
Masahiro Kato
We propose a model-grounded RAG-based AI economist with an agentic framework for economic scenario analysis using large language models (LLMs) and knowledge graphs. While LLMs can…
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…
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…