Fast Without False Precision: Foundation Models for Partial Causal Identification
TL;DR for operators Bellot and Dhir’s Foundation Models for Partial Causal Identification1 targets a specific failure mode in automated causal analysis: observational data may narrow a causal answer without determining one unique value. The proposed model is trained once to return a distribution over still-compatible causal or counterfactual answers, rather than solving a new bound-optimization problem for every dataset and query. ...