Literaturnachweis - Detailanzeige
| Autor/inn/en | Sales, Adam C.; Prihar, Ethan; Gagnon-Bartsch, Johann; Gurung, Ashish; Heffernan, Neil T. |
|---|---|
| Titel | More Powerful A/B Testing Using Auxiliary Data and Deep Learning |
| Quelle | (2022), (4 Seiten)
PDF als Volltext |
| Zusatzinformation | Weitere Informationen |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Research Methodology; Educational Experiments; Causal Models; Computation; Electronic Learning; Statistical Inference; Data Analysis; Accuracy |
| Abstract | Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and unbiased without additional assumptions, regardless of the deep-learning model's quality. In this dataset, incorporating auxiliary data improves precision consistently and, in some cases, substantially. (As Provided). |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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