Causal Persuasion

Anastasia Burkovskaya; Egor Starkov

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arXiv · 2604.20664

Causal Persuasion

Anastasia Burkovskaya·Egor Starkov

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Abstract

Correlation is not causation... until the right variables are on the table. We propose a model of causal persuasion, in which a sender selectively discloses variables alongside a subjective causal model linking them. Persuasion requires that the model rules out rival explanations, on top of being consistent with the underlying data distribution. To establish a causal link, the sender often needs to disclose at most two well-chosen variables, whereas dispelling a perceived link, every common cause must be disclosed. This highlights a fundamental asymmetry: Establishing causality is often much easier than ruling it out.

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Anastasia Burkovskaya, Egor Starkov. 2026-04-22. Causal Persuasion. https://arxiv.org/abs/2604.20664

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