Bayesian Causal Induction
Pedro Alejandro Ortega
In: 2011 NIPS Workshop in Philosophy and Machine Learning, 16-17 Dec 2011, Sierra Nevada, Spain.
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.
|EPrint Type:||Conference or Workshop Item (Invited Talk)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Pedro Ortega|
|Deposited On:||23 January 2012|