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Causal transportability of experiments on controllable subsets of variables: Z-transportability

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Authors

Lee, Sanghack; Honavar, Vasant

Issue Date
2013
Publisher
UAI
Citation
Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013, pp.361-370
Abstract
We introduce z-transportability, the problem of estimating the causal effect of a set of variables X on another set of variables Y in a target domain from experiments on any subset of controllable variables Z where Z is an arbitrary subset of observable variables V in a source domain. z-Transportability generalizes z-identifiability, the problem of estimating in a given domain the causal effect of X on Y from surrogate experiments on a set of variables Z such that Z is disjoint from X. z-Transportability also generalizes transportability which requires that the causal effect of X on Y in the target domain be estimable from experiments on any subset of all observable variables in the source domain. We first generalize z-identifiability to allow cases where Z is not necessarily disjoint from X. Then, we establish a necessary and sufficient condition for z-transportability in terms of generalized z-identifiability and transportability. We provide a sound and complete algorithm that determines whether a causal effect is z-transportable; and if it is, produces a transport formula, that is, a recipe for estimating the causal effect of X on Y in the target domain using information elicited from the results of experimental manipulations of Z in the source domain and observational data from the target domain. Our results also show that do-calculus is complete for z-transportability.
URI
https://hdl.handle.net/10371/201572
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  • Graduate School of Data Science
Research Area Causal Decision Making, Causal Discovery, Causal Inference

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