Causal Estimation of Share-Induced Engagement with Flywheel Effects

2026-07-12

AI Summary

This study addresses the challenge posed by sharing-induced network interference on online platforms, where viral sharing triggers a flywheel effect that violates the independence assumption of traditional A/B tests and biases causal effect estimation. The authors propose the first global estimator for sharing-driven engagement, modeling user participation as a geometric amplification process derived from traffic-balance identities. By integrating attribution logs, the method performs multi-round propagation correction and user-level reactivation measurement. It combines a geometric amplification model, Poisson approximation, and an A/A testing validation framework to ensure robustness. Extensive simulations and large-scale real-platform experiments demonstrate that the proposed approach substantially reduces estimation bias, effectively controls Type I error rates, and outperforms standard benchmarks such as simple mean differences and first-order adjustment methods.

Abstract

Sustainable user growth in online platforms depends not only on acquiring new users but also on reactivating and engaging existing ones through social sharing features. A well-designed sharing feature can trigger a self-reinforcing ``flywheel effect'': reactivated users become potential sharers whose engagement propagates through the network over multiple rounds, amplifying total engagement. Measuring the causal impact of such sharing features is challenging, as their effects unfold through complex social networks and temporal cascades, violating the no-interference assumption underlying classical A/B testing. We develop a framework for experiments on sharing features that accounts for interference caused by the flywheel effect and targets a global treatment effect on share-induced engagement. Our estimator is motivated by a flow-balance identity and interprets share-induced engagement as a geometric amplification process, yielding a closed-form propagation adjustment that accounts for multi-round diffusion using commonly available attribution logs. Under mild conditions, we establish consistency of the proposed estimator and develop a valid A/A testing procedure for pipeline validation. Simulation studies show that our method substantially reduces bias relative to the difference-in-means estimator and first-order adjustments, while the proposed A/A test maintains nominal Type I error. We also extend the framework to a user-level reactivation metric via a Poisson approximation. Finally, we demonstrate the approach on a real-world large-scale online platform and discuss empirical implications for evaluating sharing feature designs.

Problem

• causal estimation
• flywheel effect
• interference
• share-induced engagement
• online platforms

Innovation

• causal inference
• flywheel effect
• interference
• share-induced engagement
• propagation adjustment