Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals

Many canonical models in causal inference and structural econometrics have recursive identification formulas. In causal inference, recursion arises when identification requires both pre- and post-treatment covariates. For example, short-term surrogate outcomes are measured after the treatment, and serve as necessary covariates when identifying long-term effects. Post-treatment covariates are also required for identification of dynamic difference-in-differences designs, time-varying treatment regimes, and mediation analysis. In structural econometrics, recursion arises through evolving state variables, for example in dynamic sample selection models and dynamic discrete choice models. In this paper, we propose an automatic and recursive method for inference, applicable to such formulas, allowing for flexible estimation by neural networks and random forests. As a technical contribution, we introduce recursive Riesz representers.