CDSP uses an effect-size asymmetry assumption and statistical power to estimate causal directions from bivariate data with uncertainty, reducing false discoveries by 18% on 100 benchmark pairs.
proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining , pages=
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Causality provides a unifying framework for resolving trade-offs in trustworthy AI by managing invariance conflicts under changes to the data-generating process.
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Causal Discovery via Statistical Power (CDSP)
CDSP uses an effect-size asymmetry assumption and statistical power to estimate causal directions from bivariate data with uncertainty, reducing false discoveries by 18% on 100 benchmark pairs.
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Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Causality provides a unifying framework for resolving trade-offs in trustworthy AI by managing invariance conflicts under changes to the data-generating process.