Foundations of Causal Discovery (Eberhardt, 2017)

This post a review of the paper: Introduction to the Foundations of Causal Discovery. (Eberhardt, 2017)

Causal relationships are important because it shows us how a system behaves under intervention. A causal relationship is usually studied under an experimental setting such as a randomized controlled trial “each individual in the experiment is randomly assigned to either the treatment or control group”. A randomized controlled trial gets rid of the effects of confound variables that are the common causes of the targeted variables. A do operator is introduced $p(y|do(x))$ to distinguish the interventional conditional probability from the observational conditional probability $p(y|x)$.

(To be continued…)

Eberhardt, F., 2017. Introduction to the foundations of causal discovery. Int J Data Sci Anal 3, 81–91. https://doi.org/10.1007/s41060-016-0038-6