2019
UAI
UAI 2019
On Open-Universe Causal Reasoning
Abstract
We extend two kinds of causal models, structural equation models and simulation models, to infinite variable spaces. This enables a semantics of counterfactuals, calculus of intervention, and axiomatization of causal reasoning for rich, expressive generative models—including those in which a causal representation exists only implicitly—in an open-universe setting. Further, we show that under suitable restrictions the two kinds of models are equivalent, perhaps surprisingly since their conditional logics differ substantially in the general case. We give a series of complete axiomatizations in which the open-universe nature of the setting is seen to be essential.
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Conference Pioneer
— UAI 2019
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Interdisciplinary Bridge
— Artificial Intelligence and Knowledge & Reasoning
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Trend Setter
— Causal Inference
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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, Robotics, Speech & Audio
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Keyword Pioneer
— open-universe reasoning