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Arxiv
1d
52
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A Causal Inference Framework for Data Rich Environments
- A formal model for counterfactual estimation with unobserved confounding in data-rich environments has been proposed.
- The model combines the structural causal model view with the latent factor model view of causal inference.
- Classic models for potential outcomes and treatment assignments fit within this framework.
- The study establishes consistency of estimators for various causal parameters.
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