Robust Inference for the Direct Average Treatment Effect with Treatment Assignment Interference

2025-02-18

AI Summary

This paper addresses robust inference for the direct average treatment effect (DATT) under network interference, relaxing the conventional “no-interference” assumption. We consider settings where both treatment assignment and potential outcomes are subject to stochastic interference across units. To model such interference, we propose an Ising-model-based framework. We develop pointwise and uniform distributional approximation theories and introduce the De-Finetti Machine—a novel procedure that achieves conditional i.i.d. Gaussianization. Integrating the Hájek estimator with a new robust resampling scheme, we construct confidence intervals that are fully robust to unknown interference strength. Theoretically, our method is proven to retain asymptotic validity under arbitrary interference levels. This advances causal inference in complex networked environments by substantially improving reliability and applicability without requiring prior knowledge of interference structure or magnitude.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Uncertainty quantification in causal inference settings with random network interference is a challenging open problem. We study the large sample distributional properties of the classical difference-in-means Hajek treatment effect estimator, and propose a robust inference procedure for the (conditional) direct average treatment effect, allowing for cross-unit interference in both the outcome and treatment equations. Leveraging ideas from statistical physics, we introduce a novel Ising model capturing interference in the treatment assignment, and then obtain three main results. First, we establish a Berry-Esseen distributional approximation pointwise in the degree of interference generated by the Ising model. Our distributional approximation recovers known results in the literature under no-interference in treatment assignment, and also highlights a fundamental fragility of inference procedures developed using such a pointwise approximation. Second, we establish a uniform distributional approximation for the Hajek estimator, and develop robust inference procedures that remain valid regardless of the unknown degree of interference in the Ising model. Third, we propose a novel resampling method for implementation of robust inference procedure. A key technical innovation underlying our work is a new extit{De-Finetti Machine} that facilitates conditional i.i.d. Gaussianization, a technique that may be of independent interest in other settings.

Problem

• Develops robust inference for direct average treatment effect
• Addresses interference in treatment assignment and outcomes
• Proposes novel Ising model for complex dependencies

Innovation

• Uses Ising model for treatment dependencies
• Develops robust uniform inference procedures
• Introduces conditional i.i.d. Gaussianization method