Toward learning graphical and causal process models
Christopher Meek · 2014
We describe an approach to learning causal models that leverages temporal information. We posit the existence of a graphical de-scription of a causal process that generates observations through time. We explore as-sumptions connecting the graphical descrip-tion with the statistical process and what one can infer about the causal structure of the process under these assumptions. 1