Causal Change Point Detection and Localization

TL;DR

This paper introduces a novel method for detecting and localizing causal change points in time series data, focusing on changes in the causal relationship between variables, with applications demonstrated on simulated data.

Contribution

It proposes a new framework for identifying causal change points without relying on causal models, using invariance principles and integrating with existing change point algorithms.

Findings

Effective detection and localization of CCPs demonstrated on simulations.

Method leverages invariance to identify causal relationship changes.

Compatible with existing multiple change point detection algorithms.

Abstract

Detecting and localizing change points in sequential data is of interest in many areas of application. Various notions of change points have been proposed, such as changes in mean, variance, or the linear regression coefficient. In this work, we consider settings in which a response variable $Y$ and a set of covariates $X=(X^1,\ldots,X^{d+1})$ are observed over time and aim to find changes in the causal mechanism generating $Y$ from $X$. More specifically, we assume $Y$ depends linearly on a subset of the covariates and aim to determine at what time points either the dependency on the subset or the subset itself changes. We call these time points causal change points (CCPs) and show that they form a subset of the commonly studied regression change points. We propose general methodology to both detect and localize CCPs. Although motivated by causality, we define CCPs without referencing an underlying causal model. The proposed definition of CCPs exploits a notion of invariance, which is a purely observational quantity but -- under additional assumptions -- has a causal meaning. For CCP localization, we propose a loss function that can be combined with existing multiple change point algorithms to localize multiple CCPs efficiently. We evaluate and illustrate our methods on simulated datasets.