Causal Transportability of Experiments on Controllable Subsets of Variables: zTransportability
Sanghack Lee, Vasant Honavar
Abstract:
We introduce ztransportability, 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. zTransportability generalizes zidentifiability, 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;. zTransportability 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 zidentifiability to allow cases where Z is not necessarily disjoint from X. Then, we establish a necessary and sufficient condition for ztransportability in terms of generalized zidentifiability and transportability. We provide a correct and complete algorithm that determines whether a causal effect is ztransportable; 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 docalculus is complete for ztransportability.
Keywords:
Pages: 361370
PS Link:
PDF Link: /papers/13/p361lee.pdf
BibTex:
@INPROCEEDINGS{Lee13,
AUTHOR = "Sanghack Lee
and Vasant Honavar",
TITLE = "Causal Transportability of Experiments on Controllable Subsets of Variables: zTransportability",
BOOKTITLE = "Proceedings of the TwentyNinth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "361370"
}

