Causal Persuasion

TL;DR

This paper introduces a model of causal persuasion where a sender attempts to convince a receiver of causal links by selectively disclosing data and models, revealing an asymmetry between establishing and debunking causality.

Contribution

It characterizes conditions under which causal persuasion succeeds or fails and highlights the asymmetry between establishing and refuting causal claims.

Findings

Establishing causality often requires disclosing only one or two key variables.

Refuting causality typically demands revealing all common causes.

Persuasion success depends on the receiver's pre-existing beliefs and the data disclosed.

Abstract

We propose a model of causal persuasion, in which a sender selectively discloses a set of variables together with their true joint distribution and proposes a subjective causal model that binds them. A receiver is persuaded by this model only if the data conclusively identifies the causal link of interest. We characterize when such persuasion succeeds or fails, and how easily it can be achieved. We further show that if the receiver holds a pre-existing subjective model, debunking it is similar to persuading a receiver without one. To establish a true causal link, the sender often needs to disclose only one or two well-chosen variables. But to dispel a perceived link -- to persuade the receiver there is no causal relationship -- every common cause must be disclosed. Our results highlight a fundamental asymmetry in causal persuasion: Establishing causality is often much easier than ruling it out.