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Presenter: Usef Faghihi, UQTR, SHARCNET

In this presentation, I introduce a novel causal analysis methodology called
Probabilistic Variational Causal Effect (PACE) designed to evaluate the impact of both
rare and common events in observational studies. PACE quantifies the direct causal
effects by integrating total variation, which captures the purely causal component,
with interventions on varying treatment levels. This integration also incorporates the
likelihood of transitions between different treatment states. A key feature of PACE is
the parameter d, which allows the metric to emphasize less frequent treatment
scenarios when d is low, and more common treatments when d is high, providing a causal
effect function dependent on d.