Mar 15, 2012 · That result, called the causal flipping theorem, extends prior results to the effect that causal discovery cannot be reliable on a given sample ...
We prove that every causal conclu- sion drawn by a consistent causal discovery procedure can flip in orientation any number of times and with arbitrarily high ...
Rather, we view the unavoidability of causal flipping as the proper justification for causal discovery, in the sense that the best causal discovery algorithms ...
Abstract. Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data.
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This work proves that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal ...
Feb 8, 2015 · Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data.
Causal conclusions that flip repeatedly and their justification. KT Kelly, C Mayo-Wilson. arXiv preprint arXiv:1203.3488, 2012. 20, 2012. Structural chaos. C ...
Causal conclusions that flip repeatedly and their justification. KT Kelly, C Mayo-Wilson. arXiv preprint arXiv:1203.3488, 2012. 20, 2012. Structural chaos. C ...
Dec 28, 2022 · Kelly KT, Mayo-Wilson C (2010) Causal conclusions that flip repeatedly and their justification. In: Proceedings of the Twenty Sixth ...
... justification of such norms by truth-conduciveness, or learning performance. (with Conor Mayo-Wilson), "Causal Conclusions that Flip Repeatedly and their ...