Model-Based Regression Adjustment with Model-Free Covariates for Network Interference
Journal of Causal Inference, 2023
In this work, we introduce a sequential procedure to generate and select graph- and treatment-based covariates for GATE estimation under regression adjustment. 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.
Recommended citation: Han, Kevin and Ugander, Johan. “Model-based regression adjustment with model-free covariates for network interference” Journal of Causal Inference, vol. 11, no. 1, 2023, pp. 20230005.