Causal Inference in Social Platforms Under Approximate Interference Networks

TL;DR

This paper introduces a surrogate network approach and a pseudo inverse estimator to improve the estimation of treatment effects in social platforms with network interference, addressing practical challenges of unknown interference structures.

Contribution

It proposes the surrogate network framework and analyzes the pseudo inverse estimator's bias-variance trade-off, offering improved variance estimation and practical application insights.

Findings

Tighter asymptotic variance bound for the estimator.

Enhanced variance estimator outperforms the original.

Effective detection of network interference in a large-scale experiment.

Abstract

Estimating the total treatment effect (TTE) of a new feature in social platforms is crucial for understanding its impact on user behavior. However, the presence of network interference, which arises from user interactions, often complicates this estimation process. Experimenters typically face challenges in fully capturing the intricate structure of this interference, leading to less reliable estimates. To address this issue, we propose a novel approach that leverages surrogate networks and the pseudo inverse estimator. Our contributions can be summarized as follows: (1) We introduce the surrogate network framework, which simulates the practical situation where experimenters build an approximation of the true interference network using observable data. (2) We investigate the performance of the pseudo inverse estimator within this framework, revealing a bias-variance trade-off introduced by the surrogate network. We demonstrate a tighter asymptotic variance bound compared to previous studies and propose an enhanced variance estimator outperforming the original estimator. (3) We apply the pseudo inverse estimator to a real experiment involving over 50 million users, demonstrating its effectiveness in detecting network interference when combined with the difference-in-means estimator. Our research aims to bridge the gap between theoretical literature and practical implementation, providing a solution for estimating TTE in the presence of network interference and unknown interference structures.