Problem
Research questions and friction points this paper is trying to address.
Network Interference
Unbiased Treatment Effect Estimation
Cross-Fitting
Unbiased Treatment Effect Estimation under Network Interference via Neighborhood-Excluded Cross-Fitting
🤖 AI Summary
本文提出了一种邻域排除交叉拟合方法，以解决网络干扰下的无偏处理效应估计问题，确保在有限样本下保持无偏性。
📝 Abstract
Without interference, cross-fitting enables flexible covariate adjustment while preserving finite-sample unbiasedness under independent unit-level randomization. Under network interference, out-of-sample prediction alone no longer guarantees unbiasedness: assignments entering evaluation-fold Horvitz--Thompson weights may also affect outcomes in the training sample, inducing dependence between fitted predictions and those weights. We develop neighborhood-excluded cross-fitting, which constructs estimand- and design-specific training samples to restore the conditional independence needed for finite-sample unbiasedness without a correctly specified outcome model. We establish asymptotically valid design-based Wald inference for direct and indirect effects under Bernoulli randomization and for the global average treatment effect under Bernoulli cluster randomization. Neighborhood exclusion creates a trade-off in choosing the number of folds: unit-level splitting may require the number of folds to grow with average exclusion-neighborhood size, while cluster-level splitting can substantially relaxes this requirement, permitting a fixed number of folds under partial interference. For linear adjustment under Bernoulli randomization, we derive variance-optimal and confidence-interval-length-optimal procedures, establish explicit rate conditions allowing the covariate dimension to diverge, and show that the variance-optimal procedure is asymptotically no-harm. Simulations illustrate the bias from omitting neighborhood exclusion and the precision gains from adjustment. An application to a social network experiment yields confidence intervals substantially shorter than those from the unadjusted estimator.
Innovation
Methods, ideas, or system contributions that make the work stand out.
neighborhood-excluded cross-fitting
unbiased treatment effect estimation
network interference
finite-sample unbiasedness
asymptotically valid design-based inference
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