  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.
