Hi all, We hope you can join us for the Applied Statistics Workshop this Wednesday, September 29th, when we are excited to have Jamie Robins speaking on direct and indirect effects, based on joint work with Thomas Richardson. Details of his talk, an abstract, and a link to the paper are below. The workshop begins at 12 noon and we will serve a light lunch. Hope to see you there! "Pure (natural) direct and indirect effects, alternative counterfactual models, and the philosophy of science" James Robins Harvard School of Public Health September 29th, 2010, 12 noon K354 CGIS Knafel (1737 Cambridge St) Paper: http://www.iq.harvard.edu/events/sites/iq.harvard.edu.events/files/wp100.pdf Abstract: We consider four classes of graphical causal models: the Finest Fully Randomized Causally Interpretable Structured Tree Graph (FFRCISTG) of Robins (1986), the agnostic causal model of Spirtes et al. (1993), the Non-Parametric Structural Equation Model (NPSEM) of Pearl (2000), and the Minimal Counterfactual Model (MCM) which we introduce. The latter is referred to as ‘minimal’ because it imposes the minimal counterfactual independence assumptions required to identify those causal contrasts representing the effect of an ideal intervention on any subset of the variables in the graph. The causal contrasts identified by an MCM are, in general, a strict subset of those identified by a NPSEM associated with the same graph. We analyze various measures of the ‘direct’ causal effect, focusing on the pure direct effect (PDE), also called the ‘natural direct effect’. We show the PDE is a parameter that may be identified in a DAG viewed as a NPSEM, but not as an MCM. In spite of this, Pearl has given a scenario in which the PDE corresponds to the intent-to-treat parameter of a randomized experiment. We resolve this apparent paradox by showing that implicit within Pearl’s account is an extended causal DAG with additional variables in which there is a causal contrast that equals the pure direct effect of Pearl’s original NPSEM. Further, this contrast is identified from observational data on the original variables. Finally we relate our results to the work of Avin et al. (2005) on path-specific causal effects. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University url: http://people.fas.harvard.edu/~blackwel/