Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability
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 correct 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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Identifiability and Transportability in Dynamic Causal Networks
In this paper we propose a causal analog to the purely observational Dynamic Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound and complete algorithm for identification of Dynamic Causal Net- w…
Transportability from Multiple Environments with Limited Experiments: Completeness Results
This paper addresses the problem of $mz$-transportability, that is, transferring causal knowledge collected in several heterogeneous domains to a target domain in which only passive observations and limited experimental …
Surrogate Outcomes and Transportability
Identification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is suf…
Causal InferenceA General Algorithm for Deciding Transportability of Experimental Results
Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations. This article treats a particular problem of generalizability, called "transportability", defined…
General Transportability of Soft Interventions: Completeness Results
The challenge of generalizing causal knowledge across different environments is pervasive in scientific explorations, including in AI, ML, and Data Science. Experiments are usually performed in one environment (e.g., in …
Causal Inference