This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. Using techniques developed for graphical causal models, we show that a model can be decomposed into a set of submodels such that the identification problem can be solved independently in each submodel. We provide a new identification method that identifies causal effects by solving a set of algebraic equations.
Content Area: 5. Automated Reasoning
Subjects: 3.4 Probabilistic Reasoning; 9.1 Causality
Submitted: May 10, 2005