Efficient adjustment for complex covariates: Gaining efficiency with DOPE

2024-02-20

AI Summary

This paper addresses the challenge of estimating the average treatment effect (ATE) under high-dimensional, non-Euclidean covariates (e.g., text), where conventional graph-based adjustment methods are infeasible. We propose a general covariate adjustment framework that does not require explicit graphical modeling. Our key innovation is the first formal definition of *minimal sufficient outcome-predictive information*, which underpins the DOPE estimator—a unified approach integrating debiased learning, outcome-adaptive propensity score modeling, and a single-index structure to support both nonparametric and deep-learning-based flexible modeling. Theoretically, DOPE achieves double robustness and √n-consistency. Empirically, it significantly outperforms the augmented inverse probability weighting (AIPW) estimator in high-dimensional, nonlinear settings, delivering substantial improvements in estimation accuracy and stability—particularly mitigating AIPW’s efficiency degradation under complex, structured covariates.

Technology Category

• Search and Optimization: Non-convex Optimization
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Covariate adjustment is a ubiquitous method used to estimate the average treatment effect (ATE) from observational data. Assuming a known graphical structure of the data generating model, recent results give graphical criteria for optimal adjustment, which enables efficient estimation of the ATE. However, graphical approaches are challenging for high-dimensional and complex data, and it is not straightforward to specify a meaningful graphical model of non-Euclidean data such as texts. We propose an general framework that accommodates adjustment for any subset of information expressed by the covariates. We generalize prior works and leverage these results to identify the optimal covariate information for efficient adjustment. This information is minimally sufficient for prediction of the outcome conditionally on treatment. Based on our theoretical results, we propose the Debiased Outcome-adapted Propensity Estimator (DOPE) for efficient estimation of the ATE, and we provide asymptotic results for the DOPE under general conditions. Compared to the augmented inverse propensity weighted (AIPW) estimator, the DOPE can retain its efficiency even when the covariates are highly predictive of treatment. We illustrate this with a single-index model, and with an implementation of the DOPE based on neural networks, we demonstrate its performance on simulated and real data. Our results show that the DOPE provides an efficient and robust methodology for ATE estimation in various observational settings.

Problem

• Estimating average treatment effects efficiently from observational data.
• Handling high-dimensional, non-Euclidean covariates like text in causal inference.
• Improving efficiency when covariates strongly predict treatment assignment.

Innovation

• DOPE uses outcome-adapted propensity for efficient ATE estimation
• It accommodates non-Euclidean data like texts via neural networks
• Retains efficiency even with highly predictive covariates