Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased...

Area of Science:

• Statistics
• Machine Learning
• Causal Inference

Background:

• Data-adaptive regression methods have revolutionized statistical and machine learning.
• These methods offer optimal performance with flexible assumptions, avoiding parametric model misspecification bias.
• Their application in causal inference is a critical area of development.

Purpose of the Study:

• To discuss the use of data-adaptive regression in causal inference parameter estimation.
• To highlight targeted minimum loss-based estimation (TMLE) and double/debiased machine learning (DML) as key frameworks.
• To underscore the importance of semi-parametric theory in advancing these methods.

Main Methods:

• Focus on targeted minimum loss-based estimation (TMLE).
• Focus on double/debiased machine learning (DML).
• Discussion grounded in semi-parametric estimation theory.

Main Results:

• Data-adaptive regression enhances causal inference by reducing bias.
• TMLE and DML are prominent methods rooted in semi-parametric theory.
• These methods allow unrestricted use of data-adaptive regression techniques.

Conclusions:

• Data-adaptive regression is crucial for unbiased causal inference.
• TMLE and DML represent significant advancements in applying machine learning to causal inference.
• Further research is needed to explore the full potential of these methods.