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About this paper
Mansi Sood, Devavrat Shah
The authors present a perspective on using latent mixture learning to estimate causal effects from observational data when treatment groups differ in hidden composition. They explain that intervention outcomes combine type-specific outcome distributions using population shares, unlike observed-treatment groups. The article connects mixture learning to counterfactual distributions, conditional on identifiable components and structural assumptions, without establishing a generally validated estimator.
Summary by alphaXiv.
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