Simplifying debiased inference via automatic differentiation and probabilistic programming

2024-05-14

AI Summary

Constructing efficient debiased estimators traditionally requires manual derivation of the efficient influence function (EIF), a labor-intensive process with high technical barriers and poor scalability. Method: This paper introduces Dimple, the first framework that models statistical functionals as compositions of differentiable primitives satisfying a novel differentiability condition; it leverages automatic differentiation to directly generate unbiased, efficient estimators while simultaneously identifying nuisance parameters. Dimple integrates probabilistic programming with functional decomposition, eliminating the need for explicit EIF derivation. Contribution/Results: We provide an open-source Python library enabling users to define parameters, generate estimators, and perform inference in just a few lines of code. Extensive experiments demonstrate Dimple’s effectiveness across diverse causal and semiparametric models—including AIPW, DR-Learner, and doubly robust IV—significantly lowering the barrier to constructing efficient estimators without sacrificing statistical efficiency.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Probabilistic Programming
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Simplifying debiased inference via automatic differentiation
• Automating efficient estimator construction without manual derivation
• Enabling accessible efficient estimation with minimal code

Innovation

• Uses automatic differentiation for efficient estimators
• Employs probabilistic programming for parameter specification
• Requires no manual derivation of influence functions