Invited Commentary: Machine Learning in Causal Inference-How Do I Love Thee? Let Me Count the Ways

Area of Science:

• Epidemiology
• Biostatistics
• Data Science

Background:

• Machine learning (ML) offers advanced analytical capabilities for complex datasets.
• Its application in causal research is growing, but requires careful methodological consideration.
• Previous work has highlighted the potential and challenges of using ML for causal inference.

Purpose of the Study:

• To comment on the use of machine learning for causal research.
• To emphasize the necessity of formal frameworks for causal and statistical inference when applying ML.
• To illustrate potential pitfalls and provide recommendations for principled ML integration in causal analyses.

Main Methods:

• Adaptation of literary quotations to frame the discussion.
• Conceptual analysis of machine learning's role in causal inference.
• Illustration of potential errors arising from inadequate causal and statistical foundations.

Main Results:

• Machine learning applications in causal research must be grounded in established causal and statistical inference frameworks.
• Without a formal foundation, the use of ML can lead to erroneous conclusions.
• Principled integration requires careful consideration of both ML algorithms and underlying causal assumptions.

Conclusions:

• Formal frameworks for causal and statistical inference are essential for the valid application of machine learning in causal research.
• Recommendations are provided for the principled integration of ML into causal analyses.
• Further work is needed to refine methods for using ML in causal inference.