Estimation and Testing Methods for Causal Inference with Interference

This dissertation offers new methodologies and theoretical results to address key issues in causal inference with interference. Specifically, we develop inferential results for causal effect estimators in panel experiments under interference, introduce novel estimation methods for causal effects with network experiments and tackle the problem of detecting interference in online controlled experiments with increasing allocation.

The dissertation is here

Recommended citation: Han, K. (2023). Estimation and Testing Methods for Causal Inference with Interference. Stanford University.