activity
20242026
collaborators

9 papers

stat.ME2026

Identification and Bounding of Central Moments of Causal Effects Using Marginal Moments Information

Naoya Hashimoto, Yuta Kawakami, Jin Tian

Evaluating the causal effect of a treatment on an outcome is a central objective in causal inference. While the average causal effect summarizes the mean impact of treatment, the c…

stat.ML2026

Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis

Harsh Shrivastava, Yuta Kawakami, Junpei Komiyama +1

This paper studies the problem of identifying the treatment that maximizes the expected natural direct potential outcome (NDPO), which captures the potential outcome of an interven…

stat.ME2026

Cumulative Natural Direct and Indirect Effects for Causal Mediation Analysis

Yuta Kawakami, Jin Tian

Causal mediation analysis provides a fundamental framework for quantifying the contributions of different pathways from a treatment to an outcome through a mediator. The na…

stat.ML2026

Bounds and Identification of Joint Probabilities of Potential Outcomes and Observed Variables under Monotonicity Assumptions

Naoya Hashimoto, Yuta Kawakami, Jin Tian

Evaluating joint probabilities of potential outcomes and observed variables, and their linear combinations, is a fundamental challenge in causal inference. This paper addresses the…

stat.ME2026

Measures for Assessing Causal Effect Heterogeneity Unexplained by Covariates

Yuta Kawakami, Jin Tian

There has been considerable interest in estimating heterogeneous causal effects across individuals or subpopulations. Researchers often assess causal effect heterogeneity based on…

cs.AI2025

Potential Outcome Rankings for Counterfactual Decision Making

Yuta Kawakami, Jin Tian

Counterfactual decision-making in the face of uncertainty involves selecting the optimal action from several alternatives using causal reasoning. Decision-makers often rank expecte…