The goal of this paper is to develop a method that can more effectively detect treatment effects in randomized controlled experiments that are run inside online ...
In this work, we describe a new statistical method to improve the detection of treatment effects in interventions. We call our method TAME (Trained Across ...
Patikorn, T., Selent, D., Beck, J., Heffernan, N., and Zhou, J. "Using a Single Model Trained Across Multiple Experiments to Improve the Detection of Treatment ...
I found that mean-centering “prior” data and treating each experiment as equally important allow TAME to detect treatment effects better. In addition, we did ...
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Jan 11, 2024 · Ensemble learning is a powerful technique for improving the accuracy and reliability of prediction models, especially in scenarios where individual models may ...
Uplift modeling [1]–[8] is a technique to estimate and predict the individual-level or subgroup-level causal effects of different treatments in an experiment.
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Dec 20, 2023 · In this review, we discuss some of the challenges and opportunities for machine learning to estimate CATE, including ensuring identification assumptions are ...
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