Abstract-We address the problem of transferring information learned from experiments to a different environment, in which only passive observations can be collected. We introduce a formal representation called "selection diagrams" for expressing knowledge about differences and commonalities between environments and, using this representation, we derive procedures for deciding whether effects in the target environment can be inferred from experiments conducted elsewhere. When the answer is affirmative, the procedures identify the set of experiments and observations that need be conducted to license the transport. We further discuss how transportability analysis can guide the transfer of knowledge in non-experimental learning to minimize re-measurement cost and improve prediction power.
Observational transport refers to the transport of causal relationships across environments in which only passive observations can be collected REF .
Transportability of Causal and Statistical Relations: A Formal Approach
2011 IEEE 11th International Conference on Data Mining Workshops
2011 IEEE 11th International Conference on Data Mining Workshops
Computer Science