All Times EDT
Keywords: Causal inference, machine learning
Causal inference is a critical research area with multi-disciplinary origins and applications. For decades, great effort has been devoted to the research of causal inference on binary treatments. In practice, multiple treatment comparisons are also common in observational studies. Within the potential outcomes framework, I propose a generalized cross-fitting estimator (GCF), which generalizes the doubly robust estimator with cross-fitting for binary treatment to multiple treatment settings and provides rigorous proofs on its statistical properties. This estimator permits the use of flexible machine learning methods to model the nuisance parts using relatively weak assumptions, while there is still a theoretical guarantee for valid statistical inference. I show the asymptotic properties of the GCF estimators, and provide the asymptotic simultaneous confidence intervals that achieve the semiparametric efficiency bound for average treatment effect. The performance of the estimator is accessed through simulations based on the common evaluation metrics generally considered in the causal inference literature.