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

Panayiotis Panayiotou; Özgür Şimşek

cs.LGMar 23, 2026

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

Authors: Panayiotis Panayiotou, Özgür Şimşek

Organizations: Department of Computer Science, University of Bath, UK

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.

Figures & tables

Figure 1: Causal discovery in a chain-reaction system. Directed edges represent causal responsibility rather than low-level physical forces. Holding an object in place breaks the chain reaction and deterministically suppresses all downstream activations, revealing ancestor–descendant relations.

Panel of Figure 2 — (a) Collection of environments used for evaluation.

Causal Discovery

Causal discovery infers cause-and-effect structures from observational and interventional data, distinguishing causal relationships from correlation.

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Relaxing Faithfulness with Intervention-Only Causal Discovery

Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler

Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA · Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

Causal Discovery Causal Identifiability Causal Intervention

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Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed causal relationships, then applying interventions to orient the unknown causal directions. A critical assumption for the first step is faithfulness: a requirement that causally linked variables exhibit statistical dependence. Many natural systems include buffering and stabilizing pathways that cancel out to achieve systemic robustness. This cancellation of pathways violates faithfulness, leading causal discovery algorithms to incorrectly remove causal dependencies. In this paper, we argue that hard interventions contain information about the presence/absence of causal linkage that is overlooked in the first stage of structure discovery. We show that a mild assumption -- called intervention-immediacy faithfulness -- that allows cancellations, is sufficient to nonparametrically identify causal structures with hard interventions. These results position interventions as the primary carriers of information about causal structure, which should take precedence over conditional independence testing. To flip the paradigm, we also specify equivalence classes when the identification criteria are not met due to limitations in the scope of interventions.

Abstract

Causal Discovery

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