Construction and Utilization of Mechanism-based Causal Models

This dissertation studies how the mechanism-based view of causality can assist in construction and utilization of causal models for decision support. The mechanism-based view of causality is based on the theory of causal ordering, proposed by Simon [53], which explicates causal asymmetries among variables in a self-contained set of simultaneous structural equations. I extend the theory of causal ordering to explicate causal relations in under-constrained sets of structural equations. Considering under constrained models as intermediate representations of one's understanding of decision problems, I demonstrate that a model construction process can be viewed as the process of assembling mechanisms from under-constrained models into self-contained causal models. I formalize the reversibility property of a mechanism to support changes in structure in causal models containing reversible mechanisms. I introduce algorithms for deliberating atomic actions when one considers manipulating a variable or releasing a mechanism to achieve a decision objective. In addition, I introduce the concept of search for opportunities which amounts to both identifying the set of policy variables and computing their optimal setting for a decision objective. Search for opportunities presents decision makers with a list of ranked interventions based on the value of intervention computation. I implement an interactive system called ImaGeNIe that supports mechanism-based model construction and utilization. I conduct subject experiments and find that ImaGeNIe can effectively assist users in constructing causal models for causal reasoning