Causal Inference using Probabilistic Variational Causal Effect in Observational Studies

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  Date: November 27, 12:00pm-1:00pm ET

   

  Presenters: Usef Faghihi (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.

  Recording:

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    This event is part of the Compute Ontario Colloquium Series.