  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.
