Learning Causal Direction from Repeated Observations over Time


Inferring the direction of causal relationships is notoriously difficult. We propose a new strategy for learning causal direction when observing states of variables over time. When a cause changes state, its effects will likely change, but if an effect changes state due to an exogenous factor, its observed cause will likely stay the same. In two experiments, we found that people use this strategy to infer whether X→Y vs. X←Y, and X→Y→Z vs. X←Y→Z produced a set of data. We explore a rational Bayesian and a heuristic model to explain these results and discuss implications for causal learning.

Back to Table of Contents