2015 Keynote Cassel Lecture

jpearlJudea Pearl, Professor
University of California, Los Angeles
Computer Science Department

“The Scientific Approach to Causal Inference”

 The enourmous progress of causal analysis in the past few decades can safely be attributed to a paradigmatic shift and two fundamental principles. The shift involves a commitment to represent reality in terms of data-generating processes, rather than distributions. The principles are: (1) How counterfactuals are to be computed from a data-generating process, and (2) How dependencies in the observed data can be inferred from the structure of the process. 

This talk will describe how these two principles led to remarkably powerful techniques of answering practical, yet non-trivial questions in epidemiological research. These include: policy evaluation, confounding control, transportability, sample selection bias, heterogeneity, mediation, causes-of-effects and missing data. 
 
Background material can be viewed here:

General: 
J. Pearl, Causality (Cambridge University Press, 2000)
Tutorials:
http://bayes.cs.ucla.edu/IJCAI99/
http://ftp.cs.ucla.edu/pub/stat_ser/r350.pdf
http://ftp.cs.ucla.edu/pub/stat_ser/r424.pdf
Mediation:
http://ftp.cs.ucla.edu/pub/stat_ser/R273-U.pdf
http://ftp.cs.ucla.edu/pub/stat_ser/r389.pdf
Transportability:
http://ftp.cs.ucla.edu/pub/stat_ser/r400-reprint.pdf
Missing data
http://ftp.cs.ucla.edu/pub/stat_ser/r417.pdf
http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf
Sample-selection bias: 
http://ftp.cs.ucla.edu/pub/stat_ser/r381.pdf
http://ftp.cs.ucla.edu/pub/stat_ser/r425.pdf
Heterogeneity
http://ftp.cs.ucla.edu/pub/stat_ser/r406.pdf
Causes of Effects
http://ftp.cs.ucla.edu/pub/stat_ser/r431.pdf

 Working papers:
 http://bayes.cs.ucla.edu/csl_papers.html