observational-data

FoundCause is an amortized causal discovery model that explicitly handles latent confounders and missing data, outperforming 15 existing methods on real-world datasets with a single forward pass.

This paper introduces the 'max-potency problem' for selecting cost-constrained experiments to maximize the tightening of bounds on partial causal effects. The authors propose graphical pruning criteria to reduce the search space and demonstrate the method on NHANES health data.