Path Constraints for Causal Discovery
published: Oct. 6, 2014, recorded: December 2013, views: 1646
Download slides: nipsworkshops2013_eberhardt_causal_discovery_01.pdf (774.3 KB)
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
A linear Gaussian parameterization of a causal model has the advantage that one can characterize the causal effect of an individual pathway, and that the causal effect from one variable on another decomposes into the causal effects of each connecting pathway, which themselves decompose into the causal effects of each direct cause on such a pathway. This feature, characterized in terms of so-called 'trek-rules' enables the use of efficient discovery algorithms for causal models with feedback and latent variables. These discovery procedures can be adapted to handle discrete models with a noisy-or parameterization by using a version of Patricia Cheng's Power-PC statistic.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !