12 papers
GTDD: Generative Test-Driven Development for AI Coding Agents with Adversarial Testing
Masahiro Kato
Test-driven development gives AI coding agents executable requirements for implementing software. Because these agents can adapt their implementations to the examples they observe,…
Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
Masahiro Kato, Daiki Honma, Taka Kato
Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. W…
Handover of In-Context Learning State Across Session Boundaries
Masahiro Kato, Taka Kato
This study investigates the methodological and theoretical properties of session handover in applications that use large language models. A task may continue in a new session when…
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…
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…
Generalized Riesz Regression: A Unified Framework for Debiased Machine Learning with Riesz Representer Fitting under Bregman Divergence
Masahiro Kato
Estimating the Riesz representer is central to debiased machine learning, yet the generator and representer model determine which regression directions their first-order conditions…
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…