
Identifying Causal Effects with the R Package causaleffect
Docalculus is concerned with estimating the interventional distribution...
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Causal Effect Identification from Multiple Incomplete Data Sources: A General Searchbased Approach
Causal effect identification considers whether an interventional probabi...
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A Potential Outcomes Calculus for Identifying Conditional PathSpecific Effects
The docalculus is a wellknown deductive system for deriving connection...
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Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
We prove the main rules of causal calculus (also called docalculus) for...
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Enhancing Identification of Causal Effects by Pruning
Causal models communicate our assumptions about causes and effects in re...
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Identifying Dynamic Sequential Plans
We address the problem of identifying dynamic sequential plans in the fr...
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The DoCalculus Revisited
The docalculus was developed in 1995 to facilitate the identification o...
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Identifying Causal Effects via Contextspecific Independence Relations
Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure. If the generating system induces contextspecific independence (CSI) relations, the existing identification procedures and criteria based on docalculus are inherently incomplete. We show that deciding causal effect nonidentifiability is NPhard in the presence of CSIs. Motivated by this, we design a calculus and an automated search procedure for identifying causal effects in the presence of CSIs. The approach is provably sound and it includes standard docalculus as a special case. With the approach we can obtain identifying formulas that were unobtainable previously, and demonstrate that a small number of CSIrelations may be sufficient to turn a previously nonidentifiable instance to identifiable.
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