RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests

TL;DR

This paper introduces RieszNet and ForestRiesz, innovative methods for automatic debiasing in high-dimensional causal inference using neural networks and random forests, improving estimation accuracy without requiring explicit functional forms.

Contribution

The paper presents a novel automatic debiasing approach that learns the Riesz representation of linear functionals with neural nets and random forests, applicable to arbitrary functionals without prior analytic knowledge.

Findings

Performs well compared to existing neural net methods for average treatment effect estimation.

Effective in estimating average marginal effects with continuous treatments.

Demonstrates robustness and flexibility across different causal inference tasks.

Abstract

Many causal and policy effects of interest are defined by linear functionals of high-dimensional or non-parametric regression functions. $\sqrt{n}$-consistent and asymptotically normal estimation of the object of interest requires debiasing to reduce the effects of regularization and/or model selection on the object of interest. Debiasing is typically achieved by adding a correction term to the plug-in estimator of the functional, which leads to properties such as semi-parametric efficiency, double robustness, and Neyman orthogonality. We implement an automatic debiasing procedure based on automatically learning the Riesz representation of the linear functional using Neural Nets and Random Forests. Our method only relies on black-box evaluation oracle access to the linear functional and does not require knowledge of its analytic form. We propose a multitasking Neural Net debiasing method with stochastic gradient descent minimization of a combined Riesz representer and regression loss, while sharing representation layers for the two functions. We also propose a Random Forest method which learns a locally linear representation of the Riesz function. Even though our method applies to arbitrary functionals, we experimentally find that it performs well compared to the state of art neural net based algorithm of Shi et al. (2019) for the case of the average treatment effect functional. We also evaluate our method on the problem of estimating average marginal effects with continuous treatments, using semi-synthetic data of gasoline price changes on gasoline demand.