Alessandro Berti

Estimating Causal Effects from Tabular Data with Assumption Gates and Automated Estimator Selection for Trustworthy Artificial Intelligence

Causal analysis can guide decisions by estimating how interventions change outcomes and making the assumptions behind those estimates explicit. We present the Causal World Foundation Model, a framework for answering causal questions from tabular data. It checks declared conditions, combines classical estimators, and ca...