Roland Poellinger is giving a course on causal reasoning in Bayesian networks at FAIR, the Forum for Artificial Intelligence Research in Stellenbosch, South Africa, 28-30 November, 2016. The forum with the topic “Causality: Bayesian Networks as Platform for Knowledge Representation in Science and Philosophy” is organized by the Center for Artifical Intelligence Research (CAIR) and the University of Pretoria.


In 1913 Bertrand Russell famously refused to pursue any analysis of the concept of causa-tion, because he believed that the law of causality […] is a relic of a bygone age, surviving, like the monarchy, only because it is erroneously supposed to do no, harm (ON THE NOTION OF CAUSE). With the arrival of formal methods in Philosophy, authors as David Lewis or Judea Pearl have picked up loose ends and started re-constructing the concept close to intuition. Nevertheless, there still seems to be enough room for dispute. In his seminal book CAUSALITY (2009) Pearl expresses his astonishment about the current state of affairs: We are witnessing one of the most bizarre circles in the history of science: Causality in search of a language and, simultaneously, the language of causality in search of its meaning. The two-day course “De/Encoding Cause and Effect” will focus on the relationship between probability, correlation, and causation, on the Bayes net interventionist account of causality and the limits of causal graphs, and on how a Bayesian would confirm causal hypotheses by amalgamating evidence.


1: Decoding Cause and Effect

Propensity, probability, regularity, correlation, counterfactuals, and mechanisms

2: Causal Graphs and Interventions

Causes as difference-makers, Bayesian networks and interventions, inferred causation and algorithmic aspects, type vs. token and the actual cause, modularity (and criticism)

3: Causal Paradox and the Concept of Event

Causal decision theory, Newcomb’s problem and prisoners’ dilemma, the concept of event and Cambridge change variables, quantum mechanics and locality

4: Causal Hypotheses and Evidence Amalgamation

Bayesian epistemology, Bayesian confirmation, evidence and evidence amalgamation, application: encoding the Hill guidelines for the assessment of causality in pharmacology

More information

Please find more information on FAIR 2016 and Roland Poellinger's two-day course on the forum's website here: