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We present a framework for optimizing the selection of which content to promote on digital distribution channels, consisting of: (1) A model of the decision-problem, (2) An off-policy identification and evaluation method, (3) the design of ranking policies, and (4) a causal machine learning procedure. We partner with an international newspaper and show that our optimal policy improves the newspapers outcome by 18 percent. Our work contributes by supporting the curation of digital channels.
Neural informed decision trees with applications in healthcare and pricing
Georgia Perakis, Asterios Tsiourvas
MIT, United States of America
Tree-based models have interpretability but are not able to capture complex relationships while other ML models often lack interpretability. We propose neural-informed decision trees (NIDTs) to combine predictive power with interpretability. We evaluate NIDTs on over 20 UCI datasets and show they outperform multiple ML benchmarks. We demonstrate interpretability by extracting an explainable warfarin prescription policy and show how they can be used on a pricing problem.
Deep learning based casual inference for large-scale combinatorial experiments: theory and empirical evidence
Zikun Ye1, Zhiqi Zhang2, Dennis Zhang2, Heng Zhang3, Renyu Zhang4
1University of Illinois Urbana Champaign; 2Washington University in St. Louis; 3Arizona State University; 4Chinese University of Hong Kong
Platforms run many A/B tests, but testing all combinations is impractical. DeDL, our debiased deep learning framework, estimates causal effects and identifies optimal treatments. It combines deep learning with double machine learning, yielding consistent estimators. Results on a video-sharing platform show accurate estimation and efficient iteration. DeDL enables platforms to iterate operations effectively.