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Philosophy’s Tetrad Project Awarded International AI Honor

The Tetrad Automated Causal Discovery Platform was awarded the Super Artificial Intelligence Leader (SAIL) award at the World Artificial Intelligence Conference held in Shanghai in July. The award recognizes fundamental advances in the basic theory, methods, models and platforms of artificial intelligence (AI). Tetrad, developed by Peter SpirtesClark GlymourRichard Scheines and Joe Ramsey of Carnegie Mellon University’s Philosophy Department, was one of four projects chosen for a SAIL award from over 800 nominees, including nominations from Amazon, IBM, Microsoft and Google.

“The Tetrad project, including the open-source Tetrad software package and the now standard
reference book, ‘Causation, Prediction, and Search’ (1993), are the basis for the
modern theory of causal discovery,” said Chris Meek, principal researcher at Microsoft Research. “The ideas and software that grew from this project have fundamentally shifted how researchers explore and interpret observational data.” The book has almost 8,000 citations.

The Tetrad project was started nearly 40 years ago by Glymour, then a professor of history and philosophy of science at the University of Pittsburgh and now Alumni University Professor Emeritus of Philosophy at CMU, and his doctoral students, Richard Scheines, now Bess Family Dean of the Dietrich College of Humanities and Social Sciences and a professor of philosophy at CMU, and Kevin Kelly, now professor of philosophy at CMU.

Fundamental to the work was providing a set of general principles, or axioms, for deriving testable predictions from any causal structure. For example, consider the coronavirus. Exposure to the virus causes infection, which in turn causes symptoms (Exposure –> Infection –> Symptoms). Since not all exposures result in infections, and not all infections result in symptoms, these relations are probabilistic. But if we assume that exposure can only cause symptoms through infection, the testable prediction from the axiom is that Exposure and Symptoms are independent given Infection. That is, although knowing whether someone was exposed is informative about whether they will develop symptoms, once we already know whether someone is infected or not — knowing whether they were exposed adds no extra information — a claim that can be tested statistically with data.

Causality, with its focus on modeling and reasoning about interventions, can … take the field [of AI] to the next level … the CMU group including Peter Spirtes, Clark Glymour, Richard Scheines and Joseph Ramsey was at the center of the development, not just in terms of algorithm development, but crucially also by providing Tetrad, the de facto standard in causal discovery software,” said Bernhard Schölkopf, Amazon Distinguished Scholar and chief machine learning scientist and director, Max Planck Institute for Intelligent Systems in Germany.

The original article can be found here.

It is useful to note that their contributions in conjunction with Judea Pearl’s Causality are the two most important texts on causality using Bayesian networks and AI. In 2011, Professor Judea Pearl was awarded Turing Award. In 2020, Professor Pearl was also awarded as World Leader in AI World Society (AIWS.net) by Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF). At this moment, Professor Judea Pearl also contribute on Causal Inference for AI transparency, which is one of important AIWS.net topics on AI Ethics.