A Calculus for Causal Relevance
This work provides a formal framework for causal inference, which is incremental in extending existing axiomatizations for improved reasoning in tasks like identifying causal structure from non-observational data.
The paper tackles the problem of reasoning about causal relevance by presenting a sound and complete calculus based on Pearl's functional models semantics, extending known axioms with three new ones and introducing two new inference rules for refined model subclasses.
This paper presents a sound and completecalculus for causal relevance, based onPearl's functional models semantics.The calculus consists of axioms and rulesof inference for reasoning about causalrelevance relationships.We extend the set of known axioms for causalrelevance with three new axioms, andintroduce two new rules of inference forreasoning about specific subclasses ofmodels.These subclasses give a more refinedcharacterization of causal models than the one given in Halpern's axiomatizationof counterfactual reasoning.Finally, we show how the calculus for causalrelevance can be used in the task ofidentifying causal structure from non-observational data.