Algorithmization of Counterfactuals and a Probabilistic Theory of Causality

Date: March 02, 2020

Abstract: This talk discusses a Bayesian network, a probability distribution factorized along
on a diamond DAG (four nodes). At the highest layer we use noisy Boolean functions (of two variables).
When the Fourier series of these noisy Boolean functions are used in the algorithm for counterfactuals
(Pearl) we obtain easy explicit formulae for computing counterfactual probabilities.

Causality and causal inference have been/are of interest, e.g., for cognitive science, genetic epidemiology,
philosophy and AI. Philosophers have discussed the general nature of (probabilistic ) causality. At the end of this talk
one definition of a probabilistic cause is applied on the counterfactual probabilities of the diamond DAG.

Bio: Timo Koski comes from the Department of Mathematics, KTH Royal Institute of Technology, Stockholm,
and currently visiting FCAI with Prof. Jukka Corander as host. His scientific interests are probability,
genetics, causal inference and Bayesian networks.

Speaker: Professor Timo Koski

Affiliation: Department of Mathematics, KTH University

Place of Seminar: Lecture Hall Exactum D122, University of Helsinki