Counterfactual Prediction Methods for Causal Inference in Observational Studies with Continuous Treatments
We develop methods for estimation, inference and optimization of causal effects from observational data with continuous treatments. We present a counterfactual prediction method based on the potential outcomes framework that estimates the expected value of a potential outcome given a treatment level and confounders. We show that the method identifies the average generalized treatment effect (AGTE)
