Covariate selection strategies for causal inference: Classification and comparison.

Area of Science:

• Causal Inference
• Observational Data Analysis
• Epidemiology
• Biostatistics

Background:

• Estimating causal effects from observational data requires adjusting for confounding variables.
• Covariate selection is a critical step to ensure sufficient adjustment for confounding.
• Efficiency and robustness are additional considerations in covariate selection for causal inference.

Purpose of the Study:

• To review and compare six general approaches to covariate selection for causal inference.
• To illustrate the advantages and disadvantages of different covariate selection methods using causal diagrams.
• To empirically compare the performance of these approaches in an extensive simulation study.

Main Methods:

• Review of six distinct covariate selection strategies for confounding adjustment.
• Utilized causal diagrams (Directed Acyclic Graphs) for conceptual illustration.
• Conducted an extensive simulation study to empirically evaluate method performance.

Main Results:

• Significant performance differences were observed among the reviewed covariate selection approaches.
• No single method demonstrated uniform superiority across all scenarios.
• Method performance was contingent upon the specific adjustment strategy and the underlying confounding structure.

Conclusions:

• The choice of covariate selection method significantly impacts causal effect estimation.
• Prior knowledge of causal relationships aids in selecting the most appropriate covariate selection strategy.
• A nuanced approach considering the specific research context is necessary for optimal covariate selection.