Enterprise AI Analysis
Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI
This paper establishes a formal connection between surrogate outcome models in biostatistics and economics and prediction-powered inference (PPI) in AI. It introduces recalibrated prediction-powered inference (RePPI), a more efficient approach that uses flexible machine learning for an 'imputed loss' recalibration. RePPI consistently improves efficiency over standard PPI, even with imperfect estimation of the optimal imputed loss, and achieves minimal asymptotic variance if estimated consistently. The method is convex and demonstrates significant gains in effective sample size across diverse applications by addressing modality mismatch, distribution shift, and discrete predictions.
Key Executive Impact
Leverage cutting-edge AI inference to drive strategic decisions and unlock new levels of operational efficiency.
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Efficiency Gain
Significant
Accuracy Improvement
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
24%
Average reduction in required labels to achieve same interval length as PPI/PPI++.
Enterprise Process Flow
Initial Estimator
→
Estimate Optimal Imputed Loss
→
Compute Covariance Matrix (M)
→
Compute RePPI Estimator (0^RePPI)
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Cross-Fitting (Repeat & Average)
| Feature | XY-only | Standard PPI | PPI++ | Recalibrated PPI (RePPI) |
|---|---|---|---|---|
| Efficiency Gain over Baseline | None | Conditional | Conditional, Optimal Tuning | Guaranteed, Optimal (if s* consistent) |
| Bias Handling | No | No | Limited | Adaptive Recalibration |
| Machine Learning Integration | No | Basic Imputation | Optimal Control Variates | Flexible ML for Optimal Loss |
| Convexity of Objective | Yes | Yes (if loss convex) | Yes (if loss convex) | Yes (if loss convex) |
Real-world Impact: US Census Data
In an application to US Census data, RePPI achieved significant gains. We investigated the relationship between age and wage rates, using XGBoost for predictions. By restricting training data to college graduates but inferring on the whole population (simulating distribution shift), RePPI consistently outperformed other methods. It saved over 24% of the labels required to achieve the same confidence interval length as PPI and PPI++.
Calculate Your Potential AI Impact
Estimate the significant time and cost savings your enterprise could achieve with advanced AI-powered inference.
Annual Cost Savings
$0
Annual Hours Reclaimed
0
Your Implementation Roadmap
A structured approach to integrating recalibrated prediction-powered inference into your enterprise workflows.
Phase 1: Data Preparation
Identify core business outcomes and gather relevant labeled and unlabeled data, including high-dimensional and unstructured data for pre-trained models.
Phase 2: Model Integration & Calibration
Integrate pre-trained AI models. Apply RePPI's recalibration step using flexible machine learning to learn optimal imputed losses, addressing modality mismatch, distribution shifts, and discrete predictions.
Phase 3: Robust Inference
Generate robust statistical inferences, such as confidence intervals for target parameters, with provably higher efficiency and accuracy than traditional methods or existing PPI approaches.
Phase 4: Impact Measurement & Iteration
Measure the business impact of improved inference, validate predictions against real outcomes, and iterate on models and recalibration strategies for continuous improvement.
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