THEMATIC PROGRAMS

July 25, 2014

Causal Interpretation and Identification of Conditional Independence Structures

Schedule - Seminar 1 on CAUSAL INTERPRETATION OF GRAPHICAL MODELS
September 27 - October 8, 1999


Organized by A. P. Dawid and Glenn Shafer

Abstracts and Programme for the week of October 4 to 8, 1999

We hope that each day will be dominated by informal discussions and small working groups. Speakers are urged to use their talks to get discussion going. The talks themselves should be no more than 50 minutes, but the discussion should be unlimited.


Monday, 10 am

Glenn Shafer on "Causal Logic"

Bayes nets emerged from an effort to make reasoning under uncertainty as modular as the reasoning in rule-based expert systems. Although they are very powerful, they fall far short of that modularity. In this talk I report on my current work on completely modular but rigorous causal probabilistic reasoning. This involves a logical language that permits reference to instantaneous events at different levels of specificity and constructs probabilistic judgments from judgements of the prudence of overlapping gambles.


Monday, 2 pm

Paul Holland on "Where do counterfactuals hide in statistical models?"

Counterfactual statements involve unobservable causal effects. Statistical models also involve unobservable quantities--parameters and error terms, for example. Are these two kinds of unobservable quantities related to each other? If so, how?


Tuesday, 10 am

Richard Scheines on "The Causality Lab"

At Carnegie Mellon we have been developing web-based software for teaching causal reasoning with statistical data: the Causality Lab. Students are confronted with a list of variables, and stored behind the scenes is a causal structure (Bayesian Network). They can then set-up experiments by randomly assigning values to a variable, collect data, form causal hypotheses, make predictions about independence, test their predictions, etc. By systematically removing experimental capability until students can only collect purely observational data, the limits of what can be learned become apparent.


Tuesday, 2 pm

Glenn Shafer on "Mediators"

Adjusting for a mediator (a covariate affected by the treatment) is treacherous to do but frequently done. In this talk, I suggest that such adjustment makes sense only if (1) the mediator is defined precisely and (2) has a consistent effect in other contexts or is normative. I hope participants in the discussion can help me square this formulation with their way of thinking about this issue.