Causal Inference under Low-Order Interference
Interference effects, where the treatment of one individual has an effect on the outcome of another, are pervasive in real-world settings but violate assumptions of many classical causal estimators. While the Horvitz-Thompson estimator can account for interference, it has prohibitively high variance. In this talk, we’ll survey recent approaches to improve on this variance guarantee by imposing additional structural assumptions on the potential outcomes model or the interference network. Then, I’ll introduce a class of estimators, pseudoinverse estimators, that can be adapted to any experimental design and have strong bias and variance guarantees. Finally, I’ll show how theoretical bounds on the performance of the pseudoinverse estimator can provide practical advice when selecting an experimental design.