2013
NIPS
NeurIPS 2013
Transportability from Multiple Environments with Limited Experiments
Abstract
This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a target environment, in which only limited experiments can be performed. We reduce questions of transportability from multiple domains and with limited scope to symbolic derivations in the do-calculus, thus extending the treatment of transportability from full experiments introduced in Pearl and Bareinboim (2011). We further provide different graphical and algorithmic conditions for computing the transport formula for this setting, that is, a way of fusing the observational and experimental information scattered throughout different domains to synthesize a consistent estimate of the desired effects.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Keyword Pioneer
— transportability
🐣
Hot Topic Early Bird
— causal inference
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
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Topic Pioneer
— Causal Inference
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Trend Setter
— Causal Inference
Authors
Topics
Artificial Intelligence > Core AI > Causal Inference
Machine Learning > Application Areas > Domain Adaptation
Machine Learning > Learning Paradigms > Transfer Learning
Machine Learning > Learning Types > Transfer Learning
Machine Learning > Optimization & Theory > Causal Inference
Machine Learning > Learning Paradigms > Domain Adaptation