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Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning
Estimators targeting overlap weighted effects have been proposed to address the challenge of poor overlap, and methods enabling flexible machine learning for nuisance models address model misspecification.
The Univariate Flagging Algorithm (UFA): a Fully-Automated Approach for Identifying Optimal Thresholds in Data
We demonstrate that the proposed algorithm is robust against missing data and noise, is scalable, and is easy to interpret and visualize.
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