VCDF: A Validated Consensus-Driven Framework for Time Series Causal Discovery

TL;DR

VCDF is a versatile framework that enhances the robustness and stability of time series causal discovery methods by evaluating causal relation consistency across temporal blocks, applicable to various algorithms.

Contribution

Introduces VCDF, a simple, model-agnostic layer that improves causal discovery robustness without modifying existing algorithms.

Findings

VCDF improves VAR-LiNGAM F1 scores by 0.08-0.12 on synthetic data.

Longer sequences benefit more, with up to 0.18 improvement on sequences of length 1000.

Enhanced stability and accuracy demonstrated on simulated fMRI and IT-monitoring data.

Abstract

Time series causal discovery is essential for understanding dynamic systems, yet many existing methods remain sensitive to noise, non-stationarity, and sampling variability. We propose the Validated Consensus-Driven Framework (VCDF), a simple and method-agnostic layer that improves robustness by evaluating the stability of causal relations across blocked temporal subsets. VCDF requires no modification to base algorithms and can be applied to methods such as VAR-LiNGAM and PCMCI. Experiments on synthetic datasets show that VCDF improves VAR-LiNGAM by approximately 0.08-0.12 in both window and summary F1 scores across diverse data characteristics, with gains most pronounced for moderate-to-long sequences. The framework also benefits from longer sequences, yielding up to 0.18 absolute improvement on time series of length 1000 and above. Evaluations on simulated fMRI data and IT-monitoring scenarios further demonstrate enhanced stability and structural accuracy under realistic noise conditions. VCDF provides an effective reliability layer for time series causal discovery without altering underlying modeling assumptions.