A systematic handling of causality requires a mathematical language in which causal relationships receive symbolic representation, clearly distinct from statistical associations. Two such languages have been proposed in the past: path analysis and structural equations models, used extensively in economics and the social sciences, and Lewis-Neyman-Rubin's counterfactual (or potential-response) model, used sporadically in philosophy and statistics. Each of these two languages emphasizes different aspects of the causal inference process and each has encountered conceptual difficulties and strong opposition; path analysis and structural equations because they have been greatly misused and inadequately formalized and the counterfactual framework because it has been only partially formalized and, more significantly, because it rests on esoteric and seemingly metaphysical relationships (among counterfactual variables) that bear no apparent connection to ordinary understanding of cause-effect processes.
I will propose a formal model, based on DYNAMIC structural equations, that unifies the two languages above, explicates their conceptual and mathematical bases and resolves their technical difficulties. A simple rule enables us to translate a problem back and forth, between the structural and counterfactual representations, and choose the one appropriate for analysis.
References for Judea Pearl talks
J. Pearl "On the Foundations of Structural Equation Models."; Technical Report R-244-s, UCLA, Computer Science Dpt; November 1996.
Pearl, J., "On the Testability of Causal Models with Latent and Instrumental Variables," (with appendix: Graphs, Structural Eqautions and Counterfactuals); In P. Besnard and S. Hanks (Eds.), Uncertainty in Artificial Intelligence 11\fR, Morgan Kaufmann, San Francisco, CA, 435--443, 1995.
Pearl, J., "On the Identification of Nonparametric Structural Models," Technical Report (R-207); Revision IV, November 1995; To appear in Lecture Notes Series: Latent Variable Modelling with Application to Causality, Springer-Verlag.
J. Pearl, "Causal diagrams for empirical research" (with discussion), Biometrika 82(4), Dec. 1995, pp. 669-710.
Other relevant papers can be found here.