Core Concepts
Flexible ML methods in Double Machine Learning improve causal effect estimation by adjusting for confounding relationships.
Abstract
The content discusses the evaluation of the "double/debiased machine learning" (DML) method for estimating causal effects. It reviews the traditional assumptions necessary for causal effect estimation and introduces DML as a method to relax these assumptions using flexible ML algorithms. The paper empirically evaluates DML's performance on simulated data, comparing it to traditional statistical methods and providing actionable recommendations for researchers applying DML in practice. Key highlights include:
- Introduction to causal effects estimation with observational data.
- Review of new frameworks using machine learning to relax classical assumptions.
- Evaluation of DML's performance on simulated data and real-world applications.
- Recommendations for researchers using DML in their studies.
Stats
"When estimating the effects of air pollution on housing prices in our application, we find that DML estimates are consistently larger than estimates of less flexible methods."
"From our overall results, we provide actionable recommendations for specific choices researchers must make when applying DML in practice."