Causal Inference in the empirical sciences: A Gentle Introduction

Speaker: Judea Pearl, Professor of Computer Science and Statistics, Computer Science Department, University of California, Los Angeles.


Judea Pearl is a professor of computer science and statistics at the University of California, Los Angeles.

As a graduate of the Technion, Israel, he joined the faculty of UCLA in 1970, where he currently directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, causal inference and philosophy of science. He has authored three books: Heuristics (1984), Probabilistic Reasoning (1988), and Causality (2000). 

He is a member of the National Academy of Engineering, and a Founding Fellow the American Association for Artificial Intelligence (AAAI).

Judea Pearl is the recipient of the

  • 2001 London School of Economics Lakatos Award in philosophy of science
  • 2004 ACM Alan Newell Award
  • 2008 Benjamin Franklin Medal for Computer and Cognitive Science from the Franklin Institute
  • 2011 Rumelhart award from the Cognitive Science Society
  • 2011 Harvey Prize from the Technion, Israel Institute of Technology.


In this talk, I will introduce a few basic principles and simple mathematical tools that were found useful in solving most problems involving causal inference in the health, social and behavioral sciences.

The principles are based on non-parametric structural equation models, a natural generalization of those used by econometricians in the 1950-60s, yet cast in new mathematical and semantical underpinnings.

This framework, enriched with a few ideas from logic and graph theory, gives rise to a friendly calculus of causes and counterfactuals that unifies all existing approaches to causation and enables rank and file researchers to handle complex problems in several of the sciences. These include questions of confounding, causal effect estimation, covariate selection, policy analysis, legal responsibility, mediation analysis, instrumental variables, measurement errors, selection bias, external validity and the integration of data from diverse studies.

Special emphasis will be placed on comparing the structural and potential-outcome approaches and, using illustrative examples, forming a symbiotic system that benefits from the strong features of both.


J. Pearl, Causality (Cambridge University Press, 2nd Edition (2009))



The Seminar in Science Studies

Published Dec. 5, 2011 3:16 PM - Last modified May 24, 2012 10:09 AM