Effect heterogeneity and variable selection for standardizing causal effects to a target population.

Area of Science:

• Epidemiology
• Biostatistics
• Causal Inference

Background:

• Randomized trials often lack participant diversity, limiting generalizability to clinical practice.
• Clinical decision-makers require study results applicable to their specific patient populations.

Purpose of the Study:

• To explore homogeneity conditions for standardizing causal inference results.
• To compare different standardization approaches and their covariate adjustment requirements.

Main Methods:

• Discussed homogeneity of effect measures, counterfactual outcome state transition parameters, and counterfactual distributions.
• Analyzed implications for covariate selection and computation of standardized causal effects.
• Compared traditional standardization with newer counterfactual generalizability methods.

Main Results:

• Specific homogeneity conditions can justify standardization procedures for unbiased effect estimation.
• Different homogeneity conditions have varying implications for covariate selection and standardization computation.
• Counterfactual approaches may require extensive covariate adjustment, posing practical challenges.

Conclusions:

• Homogeneity conditions are crucial for valid standardization in causal inference.
• Careful consideration of homogeneity is needed to select appropriate standardization methods and covariates.
• Balancing generalizability with practical feasibility is essential in applying these methods.