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- W2115802951 abstract "We address the problem of deciding whether a causal or probabilistic query is estimable from data corrupted by missing entries, given a model of miss-ingness process. We extend the results of Mohan et al. [2013] by presenting more general conditions for recovering probabilistic queries of the form P(y|x) and P(y, x) as well as causal queries of the form P(y|do(x)). We show that causal queries may be recoverable even when the factors in their identifying estimands are not recoverable. Specifically, we derive graphical conditions for recovering causal effects of the form P(y|do(x)) when Y and its missingness mechanism are not d-separable. Finally, we apply our results to problems of attrition and characterize the recovery of causal effects from data corrupted by attrition." @default.
- W2115802951 created "2016-06-24" @default.
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- W2115802951 date "2014-11-01" @default.
- W2115802951 modified "2023-09-24" @default.
- W2115802951 title "Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data" @default.
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- W2115802951 doi "https://doi.org/10.21236/ada614408" @default.
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