Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions

TL;DR

This paper introduces a method for causal discovery in chain-reaction systems, showing that their structure can be uniquely identified with specific interventions, and provides an estimator with strong theoretical guarantees.

Contribution

It presents a novel approach for identifying causal structures in cascade-like systems using targeted interventions, with proven finite-sample guarantees and practical effectiveness.

Findings

Reliable recovery of causal structure from few interventions

Estimator achieves exponential error decay

Observational heuristics fail with delayed or overlapping effects

Abstract

Causal discovery is challenging in general dynamical systems because, without strong structural assumptions, the underlying causal graph may not be identifiable even from interventional data. However, many real-world systems exhibit directional, cascade-like structure, in which components activate sequentially and upstream failures suppress downstream effects. We study causal discovery in such chain-reaction systems and show that the causal structure is uniquely identifiable from blocking interventions that prevent individual components from activating. We propose a minimal estimator with finite-sample guarantees, achieving exponential error decay and logarithmic sample complexity. Experiments on synthetic models and diverse chain-reaction environments demonstrate reliable recovery from a few interventions, while observational heuristics fail in regimes with delayed or overlapping causal effects.