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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Introduces CounterBench benchmark and CoIn iterative reasoning method showing LLMs perform near random on formal counterfactual tasks but improve substantially with guided backtracking.
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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.
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CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models
Introduces CounterBench benchmark and CoIn iterative reasoning method showing LLMs perform near random on formal counterfactual tasks but improve substantially with guided backtracking.