Combining Interventional and Observational Data Using Causal Reductions

Understand

Unobserved confounding is one of the main challenges when estimating causal effects.

• We propose a causal reduction method that, given a causal model, replaces an arbitrary number of possibly high-dimensional latent confounders with a single latent confounder that takes values in the same space as the treatment variable, without changing the observational and interventional distributions the causal model entails.
• This allows us to estimate the causal effect in a principled way from combined data without relying on the common but often unrealistic assumption that all confounders have been observed.
• We apply our causal reduction in three different settings.