  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.
