Causal Discovery for Irregularly Time Series with Consistency Guarantees

TL;DR

This paper introduces ReTimeCausal, an EM-based method for causal discovery in irregularly sampled time series, ensuring structural consistency and robustness against missing data.

Contribution

It proposes a novel alternating EM framework with theoretical guarantees for causal structure recovery in challenging irregular sampling scenarios.

Findings

ReTimeCausal outperforms existing methods on synthetic datasets.

It achieves higher accuracy in causal graph recovery with high missingness.

The method demonstrates effectiveness on real-world datasets.

Abstract

This paper studies causal discovery in irregularly sampled time series-a key challenge in risk-sensitive domains like finance, healthcare, and climate science, where missing data and inconsistent sampling frequencies distort causal mechanisms. The main challenge comes from the interdependence between missing data imputation and causal structure recovery: errors in imputation and structure learning can reinforce each other, leading to an inaccurate causal graph. Existing methods either impute first and then discover, or jointly optimize both via neural representation learning, but lack explicit mechanisms to ensure mutual consistency of imputation and structure learning. We address this challenge with ReTimeCausal, an EM-based framework that alternates between imputation and structure learning, which encourages structural consistency throughout the optimization process. Our framework provides theoretical consistency guarantees for structure recovery and extends classical results to settings with irregular sampling and high missingness. ReTimeCausal combines kernel-based sparse regression and structural constraints in an alternating process that updates the completed data and the causal graph in turn. Experiments on synthetic and real-world datasets show that ReTimeCausal is more effective than existing methods under challenging irregular sampling and missing data.