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    This study introduces new doubly robust estimators for causal inference and missing data. These estimators maintain robustness even when working models are misspecified, improving upon existing methods.

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    Area of Science:

    • Statistics
    • Causal Inference
    • Missing Data Analysis

    Background:

    • Doubly robust (DR) estimators offer consistent estimation when at least one of two working models is correctly specified.
    • Existing DR methods can struggle when both working models are misspecified.
    • Bias-reduced DR estimators aim to improve robustness in misspecified settings.

    Purpose of the Study:

    • To extend bias-reduced doubly robust estimation to incorporate data-adaptive estimators (infinite-dimensional nuisance parameters).
    • To develop a novel doubly robust estimator that leverages bias reduction for improved performance under model misspecification.
    • To provide theoretical guarantees for the proposed estimator under specific model assumptions.

    Main Methods:

    • Incorporation of data-adaptive estimators within a bias reduction framework.
    • Development of a bias reduction principle applied to one nuisance parameter direction.
    • Derivation of an asymptotic linearity theorem for the proposed estimator.

    Main Results:

    • The proposed doubly robust estimators demonstrate desirable finite-sample performance.
    • Simulation studies confirm the improved performance relative to existing DR estimators.
    • The asymptotic linearity theorem provides the influence function under specific model conditions.

    Conclusions:

    • The novel doubly robust estimators offer enhanced robustness, particularly when outcome models are misspecified.
    • The approach effectively integrates data-adaptive methods for improved causal inference and missing data analysis.
    • The findings suggest a valuable advancement in robust statistical estimation techniques.