2014
NeurIPS
NeurIPS 2014
Transportability from Multiple Environments with Limited Experiments: Completeness Results
Abstract
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 data can be collected. The paper first establishes a necessary and sufficient condition for deciding the feasibility of $mz$-transportability, i.e., whether causal effects in the target domain are estimable from the information available. It further proves that a previously established algorithm for computing transport formula is in fact complete, that is, failure of the algorithm implies non-existence of a transport formula. Finally, the paper shows that the do-calculus is complete for the $mz$-transportability class.
🌉
Interdisciplinary Bridge
- Artificial Intelligence and Knowledge & Reasoning
📈
Trend Setter
- Bayesian Networks
🐣
Hot Topic Early Bird
- causal inference
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Machine Learning > Application Areas > Domain Adaptation
Knowledge & Reasoning > Reasoning > Causal Inference
Machine Learning > Learning Types > Transfer Learning
Artificial Intelligence > Learning Paradigms > Domain Adaptation
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Networks