A Topological Approach to Conditional Causality
Date:
Causality is notoriously difficult to study because determining the true orientation of correlation is difficult to gaurantee. In this talk we will introduce Fujitsu Causal Discovery, an algorithm designed by Fujitsu to conver large quantities of data into a large quantity of conditional causal graphs, or causal graphs that occur given some conditions on the data set. We will then introduce three methods we develoepd to analyze these conditional causal graphs, with the goal being to understand how different conditions can alter causality.