Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
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Counterfactual metrics on semi-simulated benchmarks fail to identify the treatment effect estimators preferred by observable metrics on real datasets, with simple meta-learners outperforming specialized causal models.
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Causal Inference with Categorical Unobserved Confounder via Mixture Learning
Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
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Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
Counterfactual metrics on semi-simulated benchmarks fail to identify the treatment effect estimators preferred by observable metrics on real datasets, with simple meta-learners outperforming specialized causal models.