  We propose analyzing conditional reasoning by appeal to a notion of
intervention on a simulation program, formalizing and subsuming a number of
approaches to conditional thinking in the recent AI literature. Our main
results include a series of axiomatizations, allowing comparison between this
framework and existing frameworks (normality-ordering models, causal structural
equation models), and a complexity result establishing NP-completeness of the
satisfiability problem. Perhaps surprisingly, some of the basic logical
principles common to all existing approaches are invalidated in our causal
simulation approach. We suggest that this additional flexibility is important
in modeling some intuitive examples.
