Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction
It is often critical for prediction models to be robust to distributional shifts between training and testing data. Viewed from a causal perspective, the challenge is to distinguish the stable causal relationships from the unstable spurious correlations across shifts. We describe a causal transfer r...
Bayir, Murat Ali
|Place of publication:||
|Data of publication:||2020-10-16|
|Database:||arXiv Computer Science
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