ITSY: Causal Discovery From Irregular Time-Series Data

2026-09-20

AI Summary

本文提出ITSY方法，通过连续优化处理不规则时间序列数据中的因果发现问题，改进了在数据缺失情况下的图恢复效果。

Abstract

Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become misspecified when samples are missing. We introduce ITSY, the first continuous-optimization method for causal discovery from irregular time series under a linear model. ITSY reformulates the structural equation so that prediction uses the nearest available history rather than the possibly missing current slice, and jointly imputes missing values while learning both graphs. A weighted reconstruction objective corrects the noise transformation induced by this reformulation. Across synthetic regimes varying missingness, scale, graph density, and noise, and on a real world benchmark, ITSY consistently improves graph recovery over representative SCM-based baselines, demonstrating the effectiveness of the proposed method. The results establish a focused solution for irregular linear first-order dynamics and clarify the assumptions required for nonlinear or higher-order extensions.

Problem

• causal discovery
• irregular time series
• missing data

Innovation

• Continuous-optimization
• Irregular time series
• Causal discovery
• Missing data imputation
• Weighted reconstruction