Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910102331588608 |
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| author | Panayiotou, Panayiotis Şimşek, Özgür |
| author_facet | Panayiotou, Panayiotis Şimşek, Özgür |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22620 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions Panayiotou, Panayiotis Şimşek, Özgür Machine Learning Artificial Intelligence 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. |
| title | Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.22620 |