# Cluster-Robust Variance Estimation for Dyadic Data

arXiv: https://arxiv.org/abs/1312.3398 · HTML: https://mlsanity.com/paper/1312.3398/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2013.

## Built on by (most cited first, 6 of 6)

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- [Confidence Intervals for Error Rates in 1:1 Matching Tasks: Critical Statistical Analysis and Recommendations](https://mlsanity.com/paper/2306.01198/) (2023)
- [Inferred Generative-Process Diversity Predicts Correlated Failure Across Language Models](https://mlsanity.com/paper/2609.03422/) (2026)
- [Finding Common Ground: Graded Communal Knowledge in Bluesky Starter Packs](https://mlsanity.com/paper/2609.19549/) (2026)
- [How Sensitive Are LLM Leaderboard Claims to Hidden Model Selection?](https://mlsanity.com/paper/2609.28177/) (2026)
- [When Pair Count Is Not the Sample Size: What All-Pairs Agent Comparisons Estimate](https://mlsanity.com/paper/2609.33012/) (2026)

## Abstract (arXiv)

Dyadic data are common in the social sciences, although inference for such settings involves accounting for a complex clustering structure. Many analyses in the social sciences fail to account for the fact that multiple dyads share a member, and that errors are thus likely correlated across these dyads. We propose a nonparametric sandwich-type robust variance estimator for linear regression to account for such clustering in dyadic data. We enumerate conditions for estimator consistency. We also extend our results to repeated and weighted observations, including directed dyads and longitudinal data, and provide an implementation for generalized linear models such as logistic regression. We examine empirical performance with simulations and applications to international relations and speed dating.