Causal Discovery under Time-Varying Delays

Causal discovery aims to uncover the causal relationships among variables beyond correlations from observational data. On time series, a major challenge is nonstationarity, which is typically modeled as regime-based shifts or drifting causal strength. In contrast, we consider non-stationarity arising from varying cause-to-effect delay, and introduce a causal model that is identifiable with few assumptions. To discover causal relationships with varying causal delay in practice, we formalize the problem in terms of the algorithmic model of causation, and propose STRETCH to discover causal graphs. We show on synthetic data that our method correctly recovers the causal relationships, and show the relevance of this causal model on a case study on the impact of El Niño climatic phenomenon on Indian monsoon.