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Obtaining Accurate Probabilistic Causal Inference by Post-Processing Calibration
In this paper, we introduce a novel framework to derive calibrated probabilities of causal relationships from observational data.
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models
The method can be considered as an extension of BBQ, a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression.
Binary Classifier Calibration: Non-parametric approach
We prove three theorems showing that using a simple histogram binning post-processing method, it is possible to make a classifier be well calibrated while retaining its discrimination capability.
Binary Classifier Calibration: Bayesian Non-Parametric Approach
A set of probabilistic predictions is well calibrated if the events that are predicted to occur with probability p do in fact occur about p fraction of the time.
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