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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.
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    3. 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.
    4. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
    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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