webinar register page

Webinar banner
David Rohde - Causal Inference is (Bayesian) Inference - A beautifully simple idea that not everyone accepts
It is often argued that causal inference is a step that follows probabilistic estimation in a two step procedure, with a separate statistical estimation and causal inference step and each step is governed by its own principles. I have argued to the contrary that Bayesian decision theory is perfectly adequate to do causal inference in a single step using nothing more than Bayesian conditioning. If true this formulation greatly simplifies causal inference. I outline this beautifully simple idea and discuss why some object to it.

Mar 9, 2022 05:00 PM in Paris

* Required information


David Rohde
Researcher @Criteo AI Lab
David Rohde is the lead of the recommendation research team at Criteo AI Lab. His areas of interest include recommender systems, Bayesian machine learning and causality.