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A nonparametric Bayesian analysis of heterogeneous treatment effects in digital experimentation
[article]
2015
arXiv
pre-print
Randomized controlled trials play an important role in how Internet companies predict the impact of policy decisions and product changes. In these 'digital experiments', different units (people, devices, products) respond differently to the treatment. This article presents a fast and scalable Bayesian nonparametric analysis of such heterogeneous treatment effects and their measurement in relation to observable covariates. New results and algorithms are provided for quantifying the uncertainty
arXiv:1412.8563v4
fatcat:f63jqxjhr5h7znxka6iww77ck4