We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. 'Dimple' takes as input computer code representing a parameter of interest and outputs an efficient estimator. Unlike standard approaches, it does not require users to derive a functional derivative known as the efficient influence function. Dimple avoids this task by applying automatic differentiation to the statistical functional of interest. Doing so requires expressing this functional as a composition of primitives satisfying a novel differentiability condition. Dimple also uses this composition to determine the nuisances it must estimate. In software, primitives can be implemented independently of one another and reused across different estimation problems. We provide a proof-of-concept Python implementation and showcase through examples how it allows users to go from parameter specification to efficient estimation with just a few lines of code.

翻译：我们提出一种简化高效估计量构建的算法，使其更易被广泛受众使用。'Dimple'以表示感兴趣参数的计算机代码作为输入，输出高效估计量。与标准方法不同，该算法无需用户推导被称为高效影响函数的泛函导数。Dimple通过对感兴趣的统计泛函应用自动微分来避免这一步骤——这需要将该泛函表示为满足新型可微性条件的原语组合。该组合还用于确定需要估计的干扰参数。在软件实现中，原语可独立实现并跨不同估计问题复用。我们提供了概念验证的Python实现，并通过实例展示其如何使用户仅需寥寥数行代码即可完成从参数规范到高效估计的完整流程。