Estimating Global Average Treatment Effect under Network Interference with Partial Information

แนวคิดหลัก

The core message of this article is to present a novel estimator called UNITE that can identify the Global Average Treatment Effect (GATE) while only relying on knowledge of the superset of neighbors for any subject in the graph, rather than requiring the exact underlying network structure.

บทคัดย่อ

The article addresses the challenge of treatment effect estimation in the presence of network interference, where the treatment to one unit can affect the outcomes of other connected units. This violates the SUTVA assumption required for standard A/B testing.
The key contributions are:

The authors propose UNITE, a novel estimator that can identify the Global Average Treatment Effect (GATE) while only requiring knowledge of the superset of neighbors for any subject in the graph, rather than the exact underlying network structure. This is a more practical assumption compared to prior work that requires full knowledge of the interference graph.

Through theoretical analysis, the authors show that UNITE is unbiased and consistent, and its variance matches the mini-max optimal lower bound. They also present self-normalized and doubly robust versions of UNITE to further reduce the variance.

The authors extend UNITE to handle non-linear interference models, where the outcome can depend on higher-order network motifs beyond just pairwise interactions.

Extensive experiments on both simulated and real-world datasets demonstrate the superior performance of UNITE compared to standard estimators that assume no interference or require full knowledge of the interference graph.

The article provides a practical solution for conducting A/B tests in the presence of network interference, where the exact interference structure is often unknown in real-world applications.

สถิติ

"A/B tests are often required to be conducted on subjects that might have social connections."
"In such settings, the SUTVA assumption for randomized-controlled trials is violated due to network interference, or spill-over effects, as treatments to group A can potentially also affect the control group B."
"When the underlying social network is known exactly, prior works have demonstrated how to conduct A/B tests adequately to estimate the global average treatment effect (GATE). However, in practice, it is often impossible to obtain knowledge about the exact underlying network."

คำพูด

"In this paper, we present UNITE: a novel estimator that relax this assumption and can identify GATE while only relying on knowledge of the superset of neighbors for any subject in the graph."
"Through theoretical analysis and extensive experiments, we show that the proposed approach performs better in comparison to standard estimators."

ข้อมูลเชิงลึกที่สำคัญจาก

A/B testing under Interference with Partial Network Information

by Shiv Shankar... ที่ arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10547.pdf

สอบถามเพิ่มเติม

How can the UNITE estimator be extended to handle dynamic or time-varying interference graphs

To extend the UNITE estimator to handle dynamic or time-varying interference graphs, we can incorporate a mechanism to update the superset of neighbors (M(i)) over time. This would involve continuously monitoring the network structure and adjusting the neighborhood information as it evolves. By implementing a dynamic updating process for M(i), the UNITE estimator can adapt to changes in the interference graph over time, ensuring accurate estimation of the treatment effects in dynamic network environments.

What are the limitations of the linear and motif-based structural models used in this work, and how can they be further generalized

The limitations of the linear and motif-based structural models used in this work primarily stem from their assumptions about the functional form of potential outcomes and the nature of interference in the network. These models may not capture complex non-linear interactions or higher-order motifs that could exist in real-world scenarios. To generalize these models, one approach could be to incorporate more flexible functional forms, such as neural networks or kernel methods, to capture non-linear relationships and higher-order interactions. Additionally, exploring more sophisticated motif models that can represent a wider range of network patterns beyond the ones considered in this work could enhance the model's ability to capture diverse interference structures.

Can the UNITE framework be adapted to settings beyond A/B testing, such as causal inference in social networks or epidemiological studies

The UNITE framework can be adapted to settings beyond A/B testing, such as causal inference in social networks or epidemiological studies, by applying the same principles of estimating treatment effects in the presence of network interference. In social networks, the framework can be used to analyze the impact of interventions or policies on interconnected individuals, considering the spillover effects and indirect influences within the network. In epidemiological studies, the framework can help estimate the effectiveness of interventions in controlling the spread of diseases, accounting for the network structure and potential interference between individuals. By extending the UNITE framework to these settings, researchers can gain valuable insights into causal relationships and intervention effects in complex networked systems.