[AutoML Seminar] Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson and Arik Reuter will give a talk on their recent work “Do-PFN: In-Context Learning for Causal Effect Estimation“. Abstract Estimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth causal graph, or Read more
