The intersection of causal inference and machine learning is a rapidly
advancing field. We propose a new approach, the method of direct estimation,
that draws on both traditions in order to obtain nonparametric estimates of
treatment effects. The approach focuses on estimating the effect of
fluctuations in a treatment variable on an outcome. A tensor-spline
implementation enables rich interactions between functional bases allowing for
the approach to capture treatment/covariate interactions. We show how new
innovations in Bayesian sparse modeling readily handle the proposed framework,
and then document its performance in simulation and applied examples.
Furthermore we show how the method of direct estimation can easily extend to
structural estimators commonly used in a variety of disciplines, like
instrumental variables, mediation analysis, and sequential g-estimation.