Universal adaptability: Target-independent inference that competes with propensity scoring.

Area of Science:

• Statistics
• Machine Learning
• Algorithmic Fairness

Background:

• Gold-standard statistical conclusions rely on random sampling, which is often infeasible.
• Existing methods like propensity score reweighting enable valid inferences but require target-specific adjustments.
• A need exists for methods that allow valid inferences from source data to diverse, unspecified target populations.

Purpose of the Study:

• To develop a target-independent statistical inference approach.
• To demonstrate the efficacy of this new method compared to existing propensity scoring techniques.
• To leverage the multicalibration framework for robust inference across varied target populations.

Main Methods:

• Developed a single, source-data-based estimator for universal adaptability.
• Established a theoretical and empirical framework to evaluate the approach.
• Connected inference in unspecified target populations with the multicalibration problem in algorithmic fairness.

Main Results:

• The proposed 'universal adaptability' approach provides efficient and accurate estimates for any downstream target data.
• The target-independent method is empirically and theoretically competitive with target-specific propensity scoring.
• The multicalibration framework successfully yields valid inferences from a single source population across diverse targets.

Conclusions:

• Universal adaptability offers a powerful, flexible alternative to traditional propensity scoring for statistical inference.
• This method enables valid inferences across multiple target populations without needing separate estimators for each.
• The study highlights the utility of algorithmic fairness concepts, specifically multicalibration, in advancing statistical inference.