Bayesian Causal Induction

Details Bayesian Causal Induction Bayesian Causal Induction

2011-11-03

arxiv.org/abs/1111.0708v2

Statistics

Machine Learning

4 pages, 4 figures; 2011 NIPS Workshop on Philosophy and Machine Learning

Scientific paper

Discovering causal relationships is a hard task, often hindered by the need for intervention, and often requiring large amounts of data to resolve statistical uncertainty. However, humans quickly arrive at useful causal relationships. One possible reason is that humans extrapolate from past experience to new, unseen situations: that is, they encode beliefs over causal invariances, allowing for sound generalization from the observations they obtain from directly acting in the world. Here we outline a Bayesian model of causal induction where beliefs over competing causal hypotheses are modeled using probability trees. Based on this model, we illustrate why, in the general case, we need interventions plus constraints on our causal hypotheses in order to extract causal information from our experience.

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