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...

Full description

Saved in:
Bibliographic details
Main Author: Zeng, Shuxi
Bayir, Murat Ali
Pfeiffer, Joel
Charles, Denis
Kiciman, Emre
Format: Journal Article
Language: English
Place of publication: 16.10.2020
Data of publication: 2020-10-16
Online Access: Fulltext
Database: arXiv Computer Science
arXiv Statistics
arXiv.org
Database information Databases - DBIS