# Exact Anytime-valid Confidence Intervals for Contingency Tables and Beyond

arXiv: https://arxiv.org/abs/2203.09785 · HTML: https://mlsanity.com/paper/2203.09785/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2022.

## Built on by (most cited first, 5 of 5)

- [Is Your Imitation Learning Policy Better than Mine? Policy Comparison with Near-Optimal Stopping](https://mlsanity.com/paper/2503.10966/) (2025)
- [E-Valuating Classifier Two-Sample Tests](https://mlsanity.com/paper/2210.13027/) (2022)
- [Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters](https://mlsanity.com/paper/2408.09598/) (2024)
- [Efficient Sequential Evaluation of Large Language Models](https://mlsanity.com/paper/2607.17409/) (2026)
- [Semi-Supervised Hypothesis Testing by Betting on Predictions](https://mlsanity.com/paper/2605.28533/) (2026)

## Abstract (arXiv)

E-variables are tools for retaining type-I error guarantee with optional stopping. We extend E-variables for sequential two-sample tests to general null hypotheses and anytime-valid confidence sequences. We provide implementations for estimating risk difference, relative risk and odds-ratios in contingency tables.