Estimating Dyadic Treatment Effects with Unknown Confounders
arXiv:2405.16547
The paper introduces methods to estimate treatment effects in dyadic data when unobserved confounders may affect treatment choice, using graphon‑based kernel smoothing and conformal inference, and illustrates the approach with international trade data.
Abstract
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.