Introduces a counterfactual method to explain low document rankings in retrieval models by finding terms whose addition would improve the rank.
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
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abstract
Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.
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UNVERDICTED 2representative citing papers
Tutorial that phrases causal questions for point exposures, defines effects via potential outcomes, classifies estimation methods by confounding assumptions, and illustrates with a breastfeeding simulation learner.
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A Counterfactual Explanation Framework for Retrieval Models
Introduces a counterfactual method to explain low document rankings in retrieval models by finding terms whose addition would improve the rank.
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Formulating causal questions and principled statistical answers
Tutorial that phrases causal questions for point exposures, defines effects via potential outcomes, classifies estimation methods by confounding assumptions, and illustrates with a breastfeeding simulation learner.