Colloquium 2024 Causal Inference using Probabilistic Variational Causal Effect in Observational Studies

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.