The Center for Causal Discovery is proud to announce the release of GFCIc (Greedy Fast Causal Inference for continuous variables). GFCIc is the latest algorithm that has been optimized to work with big biomedical data and that has been added to the CCD software suite. GFCIc is available in Python, Java, R studio as well as via our Causal web application and Tetrad.
GFCI is an algorithm that takes as input a dataset of continuous variables and outputs a graphical model called a PAG, which is a representation of a set of causal networks that may include hidden confounders. The PAG that GFCIc returns serves as a data-supported hypothesis about causal relationships that exist among the variables in the dataset. Such models are intended to help scientists form hypotheses and guide the design of experiments to investigate these hypotheses. As mentioned, GFCIc does not presuppose that there are no hidden confounders.
