Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments

By Kyle Colangelo, Ying-Ying Lee. 2019

128 citations recorded by the source corpus.

Abstract We propose a doubly robust inference method for causal effects of continuous treatment variables, under unconfoundedness and with nonparametric or high-dimensional nuisance functions. Our double debiased machine learning (DML) estimators for the average dose–response function (or the average structural function) and the partial effects are asymptotically normal with nonparametric convergence rates. The first-step estimators for the nuisance conditional expectation function and the conditional density can be nonparametric or ML methods. Using a kernel-based doubly robust moment function and cross-fitting, we give high-level conditions under which the nuisance function estimators do not affect the first-order large sample distribution of the DML estimators. We provide sufficient low-level conditions for kernel, series, and deep neural networks. We justify the use of kernel to localize the continuous treatment at a given value by the Gateaux derivative. We implement various ML methods in Monte Carlo simulations and an empirical application on a job training program evaluation.

Subfields: Statistics and Probability

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Citations in the corpus

Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, background): “Prominent examples of the debiased CTE literature include Rubin and van der Laan (2006), Dı́az and van der Laan (2013), Galvao and Wang (2015), Kennedy et al. (2017), Kallus and Zhou (2018), and Colangelo and Lee (2020).”
Estimation of Conditional Average Treatment Effects With High-Dimensional Data (2019, methodology): “Prior to this work, the use of doubly robust or locally robust methods for causal inference has been considered by an extensive body of literature including, but not limited to, Imbens (1992), Robins, Mark, and Newey (1992), Robins and Rotnitzky (1995), Hahn (1998), Van der Laan and Robins (2003), H”
Entropy Balancing for Continuous Treatments (2020, background): “…under the Creative Commons Attribution 4.0 International License. and Imai 2018; Huber et al. 2020; Naimi et al. 2014; Yiu and Su 2018), machine learning (Colangelo and Lee 2020; Kennedy et al. 2017; Kreif et al. 2015; Su, Ura, and Zhang 2019; Zhu, Coffman, and Ghosh 2015) or a combination thereof.”