# Post-detection inference for sequential changepoint localization

arXiv: https://arxiv.org/abs/2502.06096 · HTML: https://mlsanity.com/paper/2502.06096/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Time-uniform, nonparametric, nonasymptotic confidence sequences](https://mlsanity.com/paper/1810.08240/) (2018)
- [Game-theoretic statistics and safe anytime-valid inference](https://mlsanity.com/paper/2210.01948/) (2022)
- [Safe Testing](https://mlsanity.com/paper/1906.07801/) (2019)
- [E-detectors: a nonparametric framework for sequential change detection](https://mlsanity.com/paper/2203.03532/) (2022)
- [Scan B-Statistic for Kernel Change-Point Detection](https://mlsanity.com/paper/1507.01279/) (2015)
- [Bregman Deviations of Generic Exponential Families](https://mlsanity.com/paper/2201.07306/) (2022)
- [The numeraire e-variable and reverse information projection](https://mlsanity.com/paper/2402.18810/) (2024)
- [Fast and Optimal Changepoint Detection and Localization using Bonferroni Triplets](https://mlsanity.com/paper/2410.14866/) (2024)

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

- [Theoretical guarantees for change localization using conformal p-values](https://mlsanity.com/paper/2510.08749/) (2025)
- [Distribution-free changepoint localization after sequential change detection](https://mlsanity.com/paper/2606.01256/) (2026)
- [Distribution-free root cause analysis](https://mlsanity.com/paper/2605.21627/) (2026)
- [ARM: Detector-Agnostic Changepoint Attribution with Finite-Sample Error Control](https://mlsanity.com/paper/2608.01691/) (2026)
- [ARC: Augmented-Rank Conformalization for Changepoint Localization --- Finite-Sample Validity and Distribution-Robust Efficiency](https://mlsanity.com/paper/2608.08424/) (2026)

## Abstract (arXiv)

This paper addresses a fundamental but largely unexplored challenge in sequential changepoint analysis: conducting inference following a detected change. We develop a very general framework to construct confidence sets for the unknown changepoint using only the data observed up to a data-dependent stopping time at which an arbitrary sequential detection algorithm declares a change. Our framework is nonparametric, making no assumption on the composite post-change class, the observation space, or the sequential detection procedure used, and is non-asymptotically valid. We also extend it to handle composite pre-change classes under a suitable assumption, and also derive confidence sets for the change magnitude in parametric settings. We provide theoretical guarantees on the width of our confidence intervals. Extensive simulations demonstrate that the produced sets have reasonable size, and slightly conservative coverage. In summary, we present the first general method for sequential changepoint localization, which is theoretically sound and broadly applicable in practice.