Neyman Jackknife: Design-Based Variance Estimation for Causal Inference under Interference

2026-04-26

AI Summary

In finite-population causal inference, interference effects preclude the existence of a universally applicable and conservative variance estimator. This work proposes the Neyman Jackknife framework, which achieves conservative inference for the variance of causal effect estimators under arbitrary interference structures by re-estimating treatment effects after omitting subsets of treatment assignments. The approach unifies the classical Neyman variance estimator under the Stable Unit Treatment Value Assumption (SUTVA) with the Newey-West heteroskedasticity-and-autocorrelation-consistent (HAC) estimator from time series analysis, offering a general and flexible blueprint for variance estimation. Numerical experiments demonstrate that the proposed framework performs comparably to, and often better than, specialized baseline methods across a range of interference settings.

Technology Category

• Machine Learning: Causal Learning
• 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
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

We propose a framework, the Neyman Jackknife, for conservative variance estimation in finite-population causal inference under interference. Our approach provides a general, flexible blueprint that enables conservative variance estimation whenever we are able to recompute our target estimator with some treatment assignments omitted. In classical settings, our approach recovers estimators closely related to the Neyman estimator under SUTVA and the Newey-West HAC variance estimator for time series. Numerical experiments suggest that our general-purpose framework yields variance estimators that can match or even surpass the performance of baselines that were purpose-built for specific applications.

Problem

• causal inference
• interference
• variance estimation
• finite population
• Neyman Jackknife

Innovation

• Neyman Jackknife
• variance estimation
• causal inference
• interference
• finite population