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Model-based regression adjustment with model-free covariates for network interference

Author

Listed:
  • Han Kevin

    (Department of Statistics, Stanford University, Stanford, California, United Status)

  • Ugander Johan

    (Department of Management Science and Engineering, Stanford University, Stanford, California, United Status)

Abstract

When estimating a global average treatment effect (GATE) under network interference, units can have widely different relationships to the treatment depending on a combination of the structure of their network neighborhood, the structure of the interference mechanism, and how the treatment was distributed in their neighborhood. In this work, we introduce a sequential procedure to generate and select graph- and treatment-based covariates for GATE estimation under regression adjustment. We show that it is possible to simultaneously achieve low bias and considerably reduce variance with such a procedure. To tackle inferential complications caused by our feature generation and selection process, we introduce a way to construct confidence intervals based on a block bootstrap. We illustrate that our selection procedure and subsequent estimator can achieve good performance in terms of root-mean-square error in several semi-synthetic experiments with Bernoulli designs, comparing favorably to an oracle estimator that takes advantage of regression adjustments for the known underlying interference structure. We apply our method to a real-world experimental dataset with strong evidence of interference and demonstrate that it can estimate the GATE reasonably well without knowing the interference process a priori.

Suggested Citation

  • Han Kevin & Ugander Johan, 2023. "Model-based regression adjustment with model-free covariates for network interference," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-29, January.
    • Handle: RePEc:bpj:causin:v:11:y:2023:i:1:p:29:n:1
    DOI: 10.1515/jci-2023-0005
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    References listed on IDEAS

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    1. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    2. Jing Cai & Alain De Janvry & Elisabeth Sadoulet, 2015. "Social Networks and the Decision to Insure," American Economic Journal: Applied Economics, American Economic Association, vol. 7(2), pages 81-108, April.
    3. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
    4. Denis Kojevnikov, 2021. "The Bootstrap for Network Dependent Processes," Papers 2101.12312, arXiv.org.
    5. J Pouget-Abadie & G Saint-Jacques & M Saveski & W Duan & S Ghosh & Y Xu & E M Airoldi, 2019. "Testing for arbitrary interference on experimentation platforms," Biometrika, Biometrika Trust, vol. 106(4), pages 929-940.
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