Most modern supervised statistical/machine learning (ML) methods are
explicitly designed to solve prediction problems very well. Achieving this goal
does not imply that these methods automatically deliver good estimators of
causal parameters. Examples of such parameters include individual regression
coefficients, average treatment effects, average lifts, and demand or supply
elasticities. In fact, estimates of such causal parameters obtained via naively
plugging ML estimators into estimating equations for such parameters can behave
very poorly due to the regularization bias. Fortunately, this regularization
bias can be removed by solving auxiliary prediction problems via ML tools.
Specifically, we can form an orthogonal score for the target low-dimensional
parameter by combining auxiliary and main ML predictions. The score is then
used to build a de-biased estimator of the target parameter which typically
will converge at the fastest possible 1/root(n) rate and be approximately
unbiased and normal, and from which valid confidence intervals for these
parameters of interest may be constructed. The resulting method thus could be
called a "double ML" method because it relies on estimating primary and
auxiliary predictive models. In order to avoid overfitting, our construction
also makes use of the K-fold sample splitting, which we call cross-fitting.
This allows us to use a very broad set of ML predictive methods in solving the
auxiliary and main prediction problems, such as random forest, lasso, ridge,
deep neural nets, boosted trees, as well as various hybrids and aggregators of
these methods.

本文提出了一种新方法，双重机器学习 (Double ML)，用于解决因变量的原因关系，利用多种主要和辅助机器学习预测模型解决辅助和主关键参数预测，通过 K 折采样交叉拟合解决过拟合问题。