Simplifying debiased inference via automatic differentiation and probabilistic programming

TL;DR

This paper presents 'Dimple', a software tool that simplifies the creation of efficient estimators in statistical inference by leveraging automatic differentiation and probabilistic programming, removing the need for manual derivation of influence functions.

Contribution

Dimple automates the construction of efficient estimators using automatic differentiation, making advanced statistical inference more accessible and easier to implement.

Findings

Successfully implements a Python prototype demonstrating ease of use.

Enables estimation with minimal code from parameter specification.

Avoids manual derivation of influence functions through automatic differentiation.

Abstract

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.