Adversarial Causal Intervention Falsification

• URL: http://arxiv.org/abs/2608.06427v1
• Date: Wed, 05 Aug 2026 20:01:26 GMT
• Title: Adversarial Causal Intervention Falsification
• Abstract summary: We study a sequential game in which a structural causal generator proposes observational and interventional distributions.<n>An adversarial experimentalist selects interventions intended to maximally falsify the generator.<n>The discriminator is indexed by an intervention and tests whether the generator reproduces the corresponding post-intervention law.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models can reproduce an observational distribution while encoding an incorrect causal structure. We study a sequential game in which a structural causal generator proposes observational and interventional distributions, while an adversarial experimentalist selects interventions intended to maximally falsify the generator. The discriminator is therefore not merely a real-versus-synthetic classifier: it is indexed by an intervention and tests whether the generator reproduces the corresponding post-intervention law. We introduce Adversarial Causal Intervention Falsification (ACIF), formulate oracle and implementable versions of the game, and distinguish three objects that are often conflated: observational fit, interventional equivalence over an admissible query class, and point identification of a structural causal model. For finite model and intervention classes, we prove: (i) an exact reduction of the adversarial objective to a worst-intervention integral probability metric; (ii) identification up to interventional equivalence, with point identification under a separating intervention family; (iii) existence of mixed-strategy equilibria; (iv) finite-sample uniform convergence and margin-based model-selection guarantees; and (v) a logarithmic elimination guarantee for a disagreement-driven sequential design under a balanced-separation condition. We also give a complete linear-Gaussian example in which two observationally indistinguishable causal directions are separated by a single well-chosen intervention. The framework clarifies what an adversarial causal discriminator can and cannot certify, and provides a principled bridge between causal generative modeling, active causal discovery, and experimental design.

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