# Estimating Dyadic Treatment Effects with Unknown Confounders

arXiv: https://arxiv.org/abs/2405.16547 · HTML: https://mlsanity.com/paper/2405.16547/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 26 May 2024.

## Builds on (most cited first, 9 of 9)

- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [Conformal Inference of Counterfactuals and Individual Treatment Effects](https://mlsanity.com/paper/2006.06138/) (2020)
- [A Consistent Histogram Estimator for Exchangeable Graph Models](https://mlsanity.com/paper/1402.1888/) (2014)
- [Estimating network edge probabilities by neighborhood smoothing](https://mlsanity.com/paper/1509.08588/) (2015)
- [Network histograms and universality of blockmodel approximation](https://mlsanity.com/paper/1312.5306/) (2013)
- [Network Data](https://mlsanity.com/paper/1912.06346/) (2019)
- [Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing](https://mlsanity.com/paper/1811.06055/) (2018)
- [Mixed-Membership of Experts Stochastic Blockmodel](https://mlsanity.com/paper/1404.0221/) (2014)
- [Network estimation via graphon with node features](https://mlsanity.com/paper/1809.00420/) (2018)

## Abstract (arXiv)

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.