activity
20182026
most citedCausal Inference with Noncompliance and Unknown Interference

2 citations · 4 across the 10 of their papers we have counts for

collaborators

10 papers

econ.EM2026

Network-Adjusted GMM Estimation under Network Uncertainty

Tadao Hoshino

This paper proposes a network-adjusted generalized method of moments (NA-GMM) estimator for social interaction models when the observed network may differ from the true interaction…

stat.ME2025

Evaluating Policy Effects under Network Interference without Network Information: A Transfer Learning Approach

Tadao Hoshino

This paper develops a sensitivity analysis framework that transfers the average total treatment effect (ATTE) from source data with a fully observed network to target data whose ne…

econ.EM2024

Estimating Dyadic Treatment Effects with Unknown Confounders

Tadao Hoshino, Takahide Yanagi

This paper proposes estimation and inference methods for assessing treatment effects with dyadic data. Under the assumption that the treatments follow an exchangeable distribution,…

econ.EM2023

Causal Interpretation of Linear Social Interaction Models with Endogenous Networks

Tadao Hoshino

This study investigates the causal interpretation of linear social interaction models in the presence of endogeneity in network formation under a heterogeneous treatment effects fr…

stat.ME2023★ 1 cited

Randomization Test for the Specification of Interference Structure

Tadao Hoshino, Takahide Yanagi

This study considers testing the specification of spillover effects in causal inference. We focus on experimental settings in which the treatment assignment mechanism is known to r…

econ.EM2021★ 1 cited

Estimating a Continuous Treatment Model with Spillovers: A Control Function Approach

Tadao Hoshino

We study a continuous treatment effect model in the presence of treatment spillovers through social networks. We assume that one's outcome is affected not only by his/her own treat…

stat.ME2021★ 2 cited

Causal Inference with Noncompliance and Unknown Interference

Tadao Hoshino, Takahide Yanagi

We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. In particular, we suppose that the form…

econ.EM2020

A Pairwise Strategic Network Formation Model with Group Heterogeneity: With an Application to International Travel

Tadao Hoshino

In this study, we consider a pairwise network formation model in which each dyad of agents strategically determines the link status between them. Our model allows the agents to hav…

econ.EM2020

Estimating Marginal Treatment Effects under Unobserved Group Heterogeneity

Tadao Hoshino, Takahide Yanagi

This paper studies treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. Using a finite mixture approach, we pr…

econ.EM2018

Treatment Effect Models with Strategic Interaction in Treatment Decisions

Tadao Hoshino, Takahide Yanagi

This study considers treatment effect models in which others' treatment decisions can affect both one's own treatment and outcome. Focusing on the case of two-player interactions,…