Causal Discovery Algorithms based on Y Structures
نویسندگان
چکیده
Discovering relationships of the form “A causally influences B” is valuable in different fields of study. These relationships are also referred to as “cause and effect” relationships where A represents the cause, and B denotes the effect. Generally, experimental studies are performed to ascertain causality where the value of a variable is set randomly and its effects measured under controlled experimental settings. However, such experiments may not be feasible due to logistical, ethical or cost considerations. We believe that discovery algorithms that can ascertain causality from observational (passively collected) data are valuable. The framework we use for causal discovery is founded on causal Bayesian networks. A causal Bayesian network (CBN) is a Bayesian network (BN) in which each arc is interpreted as a direct causal influence between a parent node (variable) and a child node, relative to the other nodes in the network (Pearl, 1991). For example, if there is a directed edge from A to B (A → B), node A is said to exert a causal influence on node B.
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