Double/Debiased Machine Learning for Treatment and Causal Parameters
处理效应与因果参数的双重／去偏机器学习AI 解读 ↓
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处理效应与因果参数的双重／去偏机器学习
- 研究问题
- 机器学习擅长预测，但直接代入因果参数的估计方程可能产生正则化偏差；如何据此构造可靠的估计与置信区间？
- 方法与思路
- 作者结合主要与辅助预测构造正交得分，并使用 K 折样本分割形成交叉拟合，以减轻正则化偏差和过拟合影响。
- 材料陈述的贡献
- 摘要称去偏估计量通常可达到根号样本量收敛速度并近似无偏、正态，且允许使用多种机器学习方法构建预测模型。
- 阅读关注点
- 应核对各类因果参数的识别条件、正交得分构造、辅助估计的收敛要求，以及交叉拟合支持有效推断的具体条件。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或被期刊接收；摘要未给出根号样本量推断的完整适用条件。
原文摘要
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.