A Probabilistic Calculus of Actions

We present a symbolic machinery that admits both probabilistic and causal information about a given domain and produces probabilistic statements about he effect of actions and the impact of observations. Thecalculus admits two types of conditioning operators: ordinary Bayes conditioning, P(y]X = z), which represents he observation X 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 some conditional probabilities may not be available.