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Prediction De-Correlated Inference: A safe approach for post-prediction inference
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In modern data analysis, it is common to use machine learning methods to predict outcomes on unlabeled datasets and then use these pseudo-outcomes in subsequent statistical inference. Inference in this setting is often called post-prediction inference. We propose a novel assumption-lean framework for statistical inference under post-prediction setting, called Prediction De-Correlated Inference (PDC). Our approach is safe, in the sense that PDC can automatically adapt to any black-box machine-learning model and consistently outperform the supervised counterparts. The PDC framework also offers easy extensibility for accommodating multiple predictive models. Both numerical results and real-world data analysis demonstrate the superiority of PDC over the state-of-the-art methods.
Forward citations
Cited by 2 Pith papers
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On the Role of Surrogates in Conformal Inference of Individual Causal Effects
Surrogate assisted conformal inference yields shorter valid prediction intervals for individual treatment effects, with the new insight that surrogates help only when measured in the target data.
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A Moment-Based Generalization to Post-Prediction Inference
A moment-based extension of post-prediction inference is proposed, but it reduces to prediction-powered inference with calibrated predictions, and simulations show mixed coverage results.
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