Exact Anytime-valid Confidence Intervals for Contingency Tables and Beyond
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2022.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 2.
5 papers.
- 2025Is Your Imitation Learning Policy Better than Mine? Policy Comparison with Near-Optimal Stoppingcited by 14
- 2022E-Valuating Classifier Two-Sample Testscited by 10
- 2024Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameterscited by 5
- 2026Efficient Sequential Evaluation of Large Language Modelscited by 3
- 2026Semi-Supervised Hypothesis Testing by Betting on Predictionscited by 0
- 2026Efficient Sequential Evaluation of Large Language Models19 Jul 2026
- 2026Semi-Supervised Hypothesis Testing by Betting on Predictions27 May 2026
- 2025Is Your Imitation Learning Policy Better than Mine? Policy Comparison with Near-Optimal Stopping14 Mar 2025
- 2024Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters18 Aug 2024
- 2022E-Valuating Classifier Two-Sample Tests24 Oct 2022