## 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.

Original language | English (US) |
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Title of host publication | Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013 |

Pages | 361-370 |

Number of pages | 10 |

State | Published - 2013 |

Event | 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 - Bellevue, WA, United States Duration: Jul 11 2013 → Jul 15 2013 |

### Other

Other | 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 |
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Country/Territory | United States |

City | Bellevue, WA |

Period | 7/11/13 → 7/15/13 |

## All Science Journal Classification (ASJC) codes

- Artificial Intelligence