Estimation and Inference on Nonlinear and Heterogeneous Effects
This addresses the need for interpretable and reliable methods to estimate nonlinear and heterogeneous treatment effects in data analysis, representing a novel method for a known bottleneck.
The paper tackles the problem of discovering complex heterogeneities in data that multiple regression cannot capture, introducing the Method of Direct Estimation and Inference (MDEI) which provides interpretable estimates with theoretical guarantees and robust uncertainty estimates, as demonstrated through simulations and an application.
Multiple regression has been the go-to method for data analysis for generations of scholars due to its transparency, interpretability, and desirable theoretical properties. However, the method's simplicity precludes the discovery of complex heterogeneities in the data. We introduce the Method of Direct Estimation and Inference (MDEI) that embraces these potential complexities, is interpretable, has desirable theoretical guarantees, and, unlike some existing methods, returns appropriate uncertainty estimates. The proposed method uses a machine learning regression methodology to estimate the observation-level effect of a treatment variable. Importantly, we introduce a robust approach to uncertainty estimates. We provide simulation evidence and an application illustrating the performance of the method.