Physics Research Conference
Recent developments in graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Paradoxes and controversies have been resolved, slippery concepts have been demystified, and practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics.
I will review concepts, principles, and mathematical tools that were found useful in this transformation, and will demonstrate their applications in several data-intensive sciences. These include questions of policy evaluation, model testing, mediation mechanisms, generalizations from experiments, and integrating data from diverse sources.
Reference: J. Pearl, Causality (Cambridge University Press, 2000,2009)
BIO: Professor Judea Pearl was born in Bnai Brak, in 1936, and served in the Nachal and received his B.Sc. In Electrical Engineering from the Technion in 1960. He earned a Master degree in Physics from Rutgers University and a Ph.D in Electrical Engineering from the Polytechnic Institute of Brooklyn in 1965. His ground-breaking research in probabilistic and causal reasoning revolutionized the way computer systems deal with uncertain information and has enabled computers to revise beliefs and update causal connections hidden in millions of observations. Pearl's theory of causation changed the way scientists understand and estimate cause-effect relationships, and has reduced causal inference to algorithmic level of analysis. His work has had a profound impact on artificial intelligence, statistics and philosophy of science, and on the application of these fields to a wide range of problems in science and engineering.
Sponsored by the Divisions of EAS, HSS and PMA