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

2026-03-23

AI Summary

This work addresses the challenge of causal discovery in dynamic systems where delayed or overlapping causal effects render traditional observational methods ineffective. Focusing on chain-reaction systems characterized by cascading activations, the authors propose a causal identification strategy based on blocking interventions: by selectively preventing component activation, the true causal structure can be uniquely determined with only a small number of targeted interventions. Theoretical analysis demonstrates that the proposed method achieves exponential error decay and logarithmic sample complexity under finite-sample conditions. Empirical evaluations on both synthetic models and diverse chain-reaction environments confirm its efficacy, substantially outperforming purely observational heuristic approaches.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• causal discovery
• chain-reaction systems
• interventional data
• causal identifiability
• dynamic systems

Innovation

• causal discovery
• chain-reaction systems
• blocking interventions
• identifiability
• sample complexity