Higher-Order Causal Message Passing for Experimentation with Complex Interference

2024-11-01

arXiv.org

AI Summary

Traditional causal effect estimation suffers from substantial bias in experimental settings—such as social networks and online marketplaces—where unknown direct or indirect interference among units violates the stable unit treatment value assumption (SUTVA). This paper proposes the Higher-order Causal Message Passing Estimator (HCMPE), the first method to embed both mean and variance moment features of temporal unit responses into a nonlinear causal message passing framework. HCMPE jointly models the co-evolution of system state dynamics and treatment interventions. It employs a learnable temporal state mapping function and leverages ground-truth network topology in simulation-based evaluation. Experiments on synthetic and real-world datasets demonstrate significant improvements in dynamic average treatment effect (ATE) estimation accuracy. Notably, HCMPE maintains strong robustness even under non-monotonic interference probability patterns. By unifying temporal response modeling with structural interference awareness, this work establishes a novel paradigm for causal inference under complex, time-varying interference.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Accurate estimation of treatment effects is essential for decision-making across various scientific fields. This task, however, becomes challenging in areas like social sciences and online marketplaces, where treating one experimental unit can influence outcomes for others through direct or indirect interactions. Such interference can lead to biased treatment effect estimates, particularly when the structure of these interactions is unknown. We address this challenge by introducing a new class of estimators based on causal message-passing, specifically designed for settings with pervasive, unknown interference. Our estimator draws on information from the sample mean and variance of unit outcomes and treatments over time, enabling efficient use of observed data to estimate the evolution of the system state. Concretely, we construct non-linear features from the moments of unit outcomes and treatments and then learn a function that maps these features to future mean and variance of unit outcomes. This allows for the estimation of the treatment effect over time. Extensive simulations across multiple domains, using synthetic and real network data, demonstrate the efficacy of our approach in estimating total treatment effect dynamics, even in cases where interference exhibits non-monotonic behavior in the probability of treatment.

Problem

• Complex Interference
• Treatment Effect Accuracy
• Social Sciences and Online Markets

Innovation

• Causal Information Transfer
• Treatment Effect Evaluation
• Complex Interference Handling