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Hokey Pokey Causal Discovery: Using Deep Learning Model Errors to Learn Causal Structure

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Abstract

While machine learning excels at learning predictive models from observational data, learning the causal mechanisms behind the observed phenomena presents the significant challenge of distinguishing true causal relationships from confounding and other potential sources of spurious correlations. Many existing algorithms for the discovery of causal structure from observational data rely on evaluating the conditional independence relationships among features to account for the effects of confounding... (read more)

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https://openreview.net/pdf?id=IZIHJ-ME9c-

0001-01-01 -