A contrastive representation learning approach that aims to separate causal from non-causal latent factors in high-dimensional treatments for unbiased CATE estimation.
Disentangling causal effects from sets of interventions in the presence of unobserved confounders
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Contrastive representations of high-dimensional, structured treatments
A contrastive representation learning approach that aims to separate causal from non-causal latent factors in high-dimensional treatments for unbiased CATE estimation.