Alternative Graphical Causal Models and the Identification of Direct Effects

• Activity Number: 214
• Type: Invited
• Date/Time: Monday, August 1, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Thomas S. Richardson*+ and James M. Robins
• Companies: University of Washington and Harvard School of Public Health
• Keywords: potential outcomes ; direct effects ; identifiability ; causal graphical model ; NPSEM ; mediation formula

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). 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 identi?ed by an MCM are, in general, a strict subset of those identi?ed by a NPSEM associated with the same graph. We analyze various measures of the 'direct' causal effect, focusing on the pure direct effect (PDE). We show the PDE is a parameter that may be identi?ed in a DAG viewed as a NPSEM, but not as an MCM. Though bounds may be obtained under the MCM. We discuss the methodological and philosophical implications of this result.