# Post Hoc Inference for Component Attribution in Multivariate Change-Point Detection

arXiv: https://arxiv.org/abs/2607.14814 · HTML: https://mlsanity.com/paper/2607.14814/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 13 of 13)

- [A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1306.4933/) (2013)
- [Testing for a Change in Mean After Changepoint Detection](https://mlsanity.com/paper/1910.04291/) (2019)
- [A Permutation-free Kernel Two-Sample Test](https://mlsanity.com/paper/2211.14908/) (2022)
- [Selective Inference for Change Point Detection in Multi-dimensional Sequences](https://mlsanity.com/paper/1706.00514/) (2017)
- [Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference](https://mlsanity.com/paper/2110.08989/) (2021)
- [Variable Selection for Kernel Two-Sample Tests](https://mlsanity.com/paper/2302.07415/) (2023)
- [Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison](https://mlsanity.com/paper/2311.01537/) (2023)
- [Theoretical guarantees for change localization using conformal p-values](https://mlsanity.com/paper/2510.08749/) (2025)
- [Improving Power by Conditioning on Less in Post-selection Inference for Changepoints](https://mlsanity.com/paper/2301.05636/) (2023)
- [Post-selection inference for quantifying uncertainty in changes in variance](https://mlsanity.com/paper/2405.15670/) (2024)
- [Inference in high-dimensional online changepoint detection](https://mlsanity.com/paper/2111.01640/) (2021)
- [Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation](https://mlsanity.com/paper/2301.03011/) (2023)
- [Variable Selection for Comparing High-dimensional Time-Series Data](https://mlsanity.com/paper/2412.06870/) (2024)

## Built on by (most cited first, 2 of 2)

- [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)

We consider the post-detection analysis of change-points for multivariate time series, with the goal of identifying which coordinates are responsible for a detected change. After a change-point has been located by an offline detection algorithm, we propose post hoc statistical procedures to determine whether the change occurs in either of two predefined blocks of coordinates or in both. Our methods rely on two-sample testing procedures with a particular focus on nonparametric tests; we provide theoretical guarantees for Type I error control. Simulations and a real-data experiment demonstrate the strong performance of the proposed procedures.