We consider the estimation of treatment effects in settings when multiple treatments, discrete or continuous, are assigned over time and treatments can have a causal effect on future outcomes or the state of the treated unit. We propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments, which can be viewed as a Neyman orthogonal (locally robust) cross-fitted version of g-estimation in the dynamic treatment regime. Our method applies to a general class of non-linear dynamic treatment models known as Structural Nested Mean Models and allows the use of machine learning methods to control for potentially high dimensional state variables, subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the structural parameters of interest. Our work is based on a recursive peeling process, typical in g-estimation, and formulates a strongly convex objective at each stage, which allows us to extend the g-estimation framework in multiple directions: i) to provide finite sample guarantees, ii) to estimate non-linear effect heterogeneity with respect to fixed unit characteristics, within arbitrary function spaces, enabling a dynamic analogue of the RLearner algorithm for heterogeneous effects, iii) to allow for high-dimensional sparse parameterizations of the target structural functions, enabling automated model selection via a recursive lasso algorithm. We also discuss dynamic treatment effect estimation in a fully non-parametric and high-dimensional setting with discrete treatments and propose a novel automated debiased machine learning method based on recursive estimation of Riesz representers.
Based on joint works with: Greg Lewis, Victor Chernozhukov, Whitney Newey and Rahul Singh