### 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) |
---|---|

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 |
---|---|

Country | United States |

City | Bellevue, WA |

Period | 7/11/13 → 7/15/13 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Artificial Intelligence

### Cite this

*Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013*(pp. 361-370)

}

*Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013.*pp. 361-370, 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, United States, 7/11/13.

**Causal transportability of experiments on controllable subsets of variables : Z-transportability.** / Lee, Sanghack; Honavar, Vasant.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Causal transportability of experiments on controllable subsets of variables

T2 - Z-transportability

AU - Lee, Sanghack

AU - Honavar, Vasant

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84888168872&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84888168872&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84888168872

SP - 361

EP - 370

BT - Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013

ER -