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HETEROGENEOUS TREATMENT AND SPILLOVER EFFECTS UNDER CLUSTERED NETWORK INTERFERENCE.
Falco J Bargagli-Stoffi1, Costanza Tortú2, Laura Forastiere3
1Department of Biostatistics, University of California, Los Angeles.
The Annals of Applied Statistics
|July 11, 2025
Summary
This study introduces a network causal tree (NCT) algorithm to analyze treatment and spillover effects in networks, accounting for interference. The method helps understand how interventions affect individuals and their connections, improving policy targeting.
Area of Science:
- Statistics
- Machine Learning
- Econometrics
Background:
- Causal inference studies often assume no interference between units.
- Real-world networks involve interconnected units where treatment effects can spill over.
- Interference within clustered networks necessitates advanced analytical methods.
Purpose of the Study:
- To develop a machine learning method to assess heterogeneous treatment and spillover effects in clustered networks with interference.
- To provide tools for policymakers to understand intervention impacts beyond direct recipients.
- To guide strategies for scaling up interventions and improving cost-effectiveness.
Main Methods:
- A novel network causal tree (NCT) algorithm combining tree-based methods and Horvitz-Thompson estimators.
- Analysis of individual, neighborhood, and network characteristics to understand effect heterogeneity.
- Monte Carlo simulation to evaluate NCT method performance.
Main Results:
- The NCT algorithm effectively assesses heterogeneous treatment and spillover effects in the presence of network interference.
- The method avoids potential bias introduced by interference in clustered networks.
- Demonstrated application in analyzing information sessions' impact on weather insurance adoption.
Conclusions:
- The NCT algorithm offers a robust approach to causal inference in networked settings with interference.
- Understanding heterogeneous spillover effects is crucial for effective public policy and intervention design.
- The method has practical applications in areas like insurance uptake and public health campaigns.
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