Estimating Dyadic Treatment Effects with Unknown Confounders

TL;DR

This paper introduces a novel statistical inference method for estimating treatment effects in dyadic data with unobserved confounders, leveraging exchangeability assumptions and graphon-based smoothing techniques.

Contribution

It develops a neighborhood kernel smoothing approach and permutation testing framework for dyadic treatment effect estimation under unobserved confounding.

Findings

Effective estimation of dyadic effects demonstrated on trade data

Proposed method controls size under regularity conditions

Convergence rates established for the estimator

Abstract

This paper proposes a statistical inference method 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 neighborhood kernel smoothing method for estimating dyadic average treatment effects. We also develop a permutation inference method for testing the sharp null hypothesis. Under certain regularity conditions, we derive the rate of convergence of the proposed estimator and demonstrate the size control property of our test. We apply our method to international trade data to assess the impact of free trade agreements on bilateral trade flows.