2024/05/26 by Tadao Hoshino, Takahide Yanagi, Hoshino, Tadao +1
Mathematics · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.2405.16547
openalex publication_date 2024/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes estimation and inference methods for assessing treatment effects with dyadic data. Under the assumption that the treatments follow an exchangeable distribution, our approach allows for the presence of any unobserved confounding factors that potentially cause endogeneity of treatment choice without requiring additional information other than the treatments and outcomes. Building on the literature of graphon estimation in network data analysis, we propose a neighbourhood kernel smoothing method for estimating dyadic average treatment effects, and derive the rate of convergence of the proposed estimator under certain regularity conditions. We also develop conformal inference methods for predicting outcomes conditional on treatment status. We apply our methods to international trade data to assess the impact of free trade agreements on bilateral trade flows.