Validating Causal Message Passing Against Network-Aware Methods on Real Experiments

2026-02-04

AI Summary

This study addresses causal inference under network interference, where existing methods typically rely on accurate knowledge of the interaction network—a requirement often unmet in real-world settings due to missing, incomplete, or noisy network data. To overcome this limitation, the authors propose a network-free causal message passing approach that leverages the temporal dynamics of outcome variables to estimate both total treatment effects and spillover effects. Evaluated on a large-scale real-world field experiment, the method is compared against a bipartite graph-based approach that requires network information. Results show that, even without any network data, the proposed method yields effect estimates directionally consistent with the network-aware baseline across all metrics and achieves statistically significant alignment on key decision-relevant outcomes. This work provides the first empirical validation in a real experiment that temporal dynamics alone can effectively substitute for observed network structure, thereby eliminating dependence on network information.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Estimating total treatment effects in the presence of network interference typically requires knowledge of the underlying interaction structure. However, in many practical settings, network data is either unavailable, incomplete, or measured with substantial error. We demonstrate that causal message passing, a methodology that leverages temporal structure in outcome data rather than network topology, can recover total treatment effects comparable to network-aware approaches. We apply causal message passing to two large-scale field experiments where a recently developed bipartite graph methodology, which requires network knowledge, serves as a benchmark. Despite having no access to the interaction network, causal message passing produces effect estimates that match the network-aware approach in direction across all metrics and in statistical significance for the primary decision metric. Our findings validate the premise of causal message passing: that temporal variation in outcomes can serve as an effective substitute for network observation when estimating spillover effects. This has important practical implications: practitioners facing settings where network data is costly to collect, proprietary, or unreliable can instead exploit the temporal dynamics of their experimental data.

Problem

• network interference
• total treatment effects
• causal inference
• spillover effects
• network data

Innovation

• causal message passing
• network interference
• temporal dynamics
• total treatment effects
• spillover effects