
Causal inference with Bayes rule
The concept of causality has a controversial history. The question of wh...
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A Calculus for Causal Relevance
This paper presents a sound and completecalculus for causal relevance, b...
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Replacing the docalculus with Bayes rule
The concept of causality has a controversial history. The question of wh...
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Sufficiency, Separability and Temporal Probabilistic Models
Suppose we are given the conditional probability of one variable given s...
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Identifiability and Transportability in Dynamic Causal Networks
In this paper we propose a causal analog to the purely observational Dyn...
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Probabilities in Session Types
This paper deals with the probabilistic behaviours of distributed system...
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Approximate Deduction in Single Evidential Bodies
Results on approximate deduction in the context of the calculus of evide...
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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 the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P(yX = x), which represents the observation X = x, and causal conditioning, P(ydo(X = x)), read the probability of Y = y conditioned on holding X constant (at x) 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.
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