Fully generative synthetic data preserves predictive utility but distorts ATE estimates due to a structural mismatch with prediction loss; a hybrid framework separating covariate generation from causal mechanisms improves fidelity.
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Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
Fully generative synthetic data preserves predictive utility but distorts ATE estimates due to a structural mismatch with prediction loss; a hybrid framework separating covariate generation from causal mechanisms improves fidelity.