
A Calculus for Causal Relevance
This paper presents a sound and completecalculus for causal relevance, b...
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Identifying Causal Effects via Contextspecific Independence Relations
Causal effect identification considers whether an interventional probabi...
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Multivariate Counterfactual Systems And Causal Graphical Models
Among Judea Pearl's many contributions to Causality and Statistics, the ...
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The DoCalculus Revisited
The docalculus was developed in 1995 to facilitate the identification o...
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Pathspecific Effects Based on Information Accounts of Causality
Pathspecific effects in mediation analysis provide a useful tool for fa...
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Causal Inference by Surrogate Experiments: zIdentifiability
We address the problem of estimating the effect of intervening on a set ...
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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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A Potential Outcomes Calculus for Identifying Conditional PathSpecific Effects
The docalculus is a wellknown deductive system for deriving connections between interventional and observed distributions, and has been proven complete for a number of important identifiability problems in causal inference. Nevertheless, as it is currently defined, the docalculus is inapplicable to causal problems that involve complex nested counterfactuals which cannot be expressed in terms of the "do" operator. Such problems include analyses of pathspecific effects and dynamic treatment regimes. In this paper we present the potential outcome calculus (pocalculus), a natural generalization of docalculus for arbitrary potential outcomes. We thereby provide a bridge between identification approaches which have their origins in artificial intelligence and statistics, respectively. We use pocalculus to give a complete identification algorithm for conditional pathspecific effects with applications to problems in mediation analysis and algorithmic fairness.
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