 As Pearl notes, a Bayesian network is nothing more than a graphical representation of a huge table of joint probabilities for the variables involved (Pearl &amp; Mackenzie 2018: 129). Given any sizable number of variables, however, calculation becomes humanly unmanageable—hence the crucial use of computers. The fact that Bayesian networks are so computationally intensive is in fact a point that Thagard makes against using them as models of human cognitive processing (Thagard 1992: 201). But that is not an objection against other philosophical interpretations. Application to philosophical questions of causality in philosophy of science is detailed in Spirtes, Glymour, and Scheines (1993) and Sprenger and Hartmann (2019). Bayesian networks are now something of a standard in artificial intelligence, ubiquitous in its applications, and powerful algorithms have been developed to extract causal networks from the massive amounts of data available.