Judea Pearl


Information and Communication Technologies

14th Edition

The BBVA Foundation Frontiers of Knowledge Award in Information and Communication Technologies has gone in this fourteenth edition to Judea Pearl for “bringing a modern foundation to artificial intelligence,” in the words of the selection committee.


The BBVA Foundation Frontiers of Knowledge Award in the Information and Communication Technologies category goes, in this fourteenth edition, to Judea Pearl for laying a mathematical foundation first for concepts of probabilistic reasoning, and later for how to infer causal relationships through observed and ordered data. His work constructed a framework not only for computer-based thought, but indeed for entire fields of scientific study spanning computer science, mathematics and statistics, epidemiology and health, and the social sciences.

People constantly make decisions amidst uncertainty, by considering outcome likelihoods and by applying concepts of cause-and-effect. Prior to the work of Judea Pearl, such intuitive approaches were very difficult to encode into digital computer systems, limiting how well computer algorithms could sift through large amounts of real-world data, or discern whether two observations were consequential or simply coincident.

Pearl’s work in probabilistic reasoning introduced the concept of a “Bayesian network,” a representation of events and their likelihood of occurrence. Such graphs enabled simple and graphical articulations of highly complex event networks and their probabilistic relationships, which enabled computers to solve real-world scenarios, discover latent dependencies, and predict outcomes through the propagation of probabilities.

Causal calculus, introduced by Pearl in his book Causality, provided a formal framework for reasoning about the assumptions under which causal relationships can be inferred from data. Practically speaking, this enables us to understand how to predict the effect of interventions on outcomes. By precisely characterizing confounding variables, and proposing methods for their derivation from data, Pearl developed a mathematical language for distinguishing between causal relations and spurious correlations.