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

Han Kevin () and Ugander Johan ()
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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

Journal of Causal Inference, 2023, vol. 11, issue 1, 29

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.

Keywords: causal inference with interference; SUTVA; A/B testing; regression adjustment; social network analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:11:y:2023:i:1:p:29:n:1

DOI: 10.1515/jci-2023-0005

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