December 5, 2025Biometrics

Flexible and efficient estimation of causal effects with error-prone exposures: a control variates approach for measurement error

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Our approach improves causal effect estimation, enhancing flexibility in the presence of measurement error.
Simulation studies illustrate our framework's robust performance against existing estimation methods.
Control variates are employed to leverage both gold-standard and error-prone data for efficient analysis.
Findings support the use of flexible methods in two-phase sampling for accurate causal inference.

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This analysis finds improved estimation methods with flexibility in causal inference, using control variates for error-prone exposures.

Barnatchez et al. (2025) studied this question.

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