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

• URL: http://arxiv.org/abs/2603.22620v1
• Date: Mon, 23 Mar 2026 22:45:15 GMT
• Title: Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions
• Abstract summary: Causal discovery is challenging in general dynamical systems because the underlying causal graph may not be identifiable even from interventional data.<n>We study causal discovery in such chain-reaction systems and show that the causal structure is uniquely identifiable from blocking interventions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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