COSTA: Covariance-Optimized Design and Causal Inference under Network-Temporal Interference

2026-09-01

AI Summary

该研究提出COSTA方法，通过优化分配协方差解决网络实验中的溢出效应和时间延续性问题，以提高估计精度。

Abstract

Experiments on networks observed over time face network spillovers, temporal carryover, and dependence deliberately introduced by the design. We propose COSTA---Covariance-Optimized Spatiotemporal Treatment Allocation---a joint Bernoulli design for unit--time assignments. Under common treatment marginals and a nonnegative linear network--temporal exposure model, Horvitz--Thompson bias for the sustained all-treated versus all-control contrast is exactly the negative expected weight of an assignment cut. A covariance-level variance envelope yields an MSE bound that can be optimized directly over assignment covariance. To scale this design, we introduce a thresholded-Gaussian Kronecker parameterization that mirrors the network and temporal exposure operators while preserving valid Bernoulli marginals. We next develop inference theory for the joint effects of designed treatment dependence and interference-induced outcome dependence. Canonical correlations between latent blocks generating separated HT contributions supply the coefficients required by graph-$ψ$ central limit and network-HAC theory; a spectral-floor and far-row-mass condition gives a primitive sufficient check. The framework covers sparse, block, Kronecker, locally factored, and other structured covariance sequences satisfying these conditions. Semi-synthetic RetailRocket and MovieLens experiments show substantial default-setting RMSE reductions and well-calibrated model-assisted design-centered intervals across linear, nonlinear, and demand-substitution outcome surfaces.

Problem

• network spillovers
• temporal carryover
• causal inference

Innovation

• COSTA
• Covariance-Optimized Design
• Network-Temporal Interference
• Bernoulli Design
• Kronecker Parameterization