A Probabilistic Calculus of Actions

Wepresenta symbolicmachinerythatadmits bothprobabilisticand causalinformation abouta givendomainand producesprobabilisticstatementsabouttheeffectofactions andtheimpactof observations.Thecalculus admitstwotypesofconditioningoperators: ordinaryBayesconditioning,P(y]X= z), whichrepresentstheobservationX - z, and causal conditioning, P(yldo(X = x)), read the probability of Y= y conditioned on holding X constant (at z) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions (and policies) from partially specified knowledge bases, such as Bayesian networks in which someconditional probabilities may not be available.

• Type: Conference
• Year: 1994
• Where: UAI
• Authors: Judea Pearl
• Keywords: Causal, Causal Conditioning, Causalinformation Abouta Givendomainand, UAI 1994, UAI 2008