Joris M. Mooij

Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions

A novel causal reduction method is proposed that replaces an arbitrary number of possibly high-dimensional latent confounders with a single latent confounder that lives in the same space as the treatment variable without changing the observational and interventional distributions entailed by the causal model.