UvA-DARE (
Christos Louizos, Kevin Swersky, Yujia Li et al.
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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.
Maximilian Ilse, Patrick Forré, Max Welling et al.
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