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：本文提出一种简化高效估计量构建过程的算法，使其能够被更广泛的研究者所使用。该算法名为"Dimple"，其输入为表示目标参数的计算机代码，输出为高效估计量。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通过将自动微分技术应用于目标统计泛函来规避这一复杂步骤。该过程需要将目标泛函表示为满足新型可微性条件的基元组合。Dimple同时利用该组合结构确定需要估计的干扰参数。在软件实现中，各基元可相互独立实现，并能在不同估计问题中重复使用。我们提供了概念验证的Python实现，并通过示例展示用户仅需数行代码即可完成从参数设定到高效估计的全过程。