Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments
Guillaume W. Basse, Hossein Azari Soufiani, Diane Lambert
Abstract
Buyers (e.g., advertisers) often have limited financial or processing resources, and so their participation in auctions is throttled. Changes to auctions may affect bids or throttling, and any change may affect what buyers pay. This paper shows that if an A/B experiment affects only bids, then the observed treatment effect is an unbiased estimator when all the bidders in the same auction are randomly assigned to A or B but the observed treatment effect can be severely biased otherwise, even in the absence of throttling. Experiments that affect throttling algorithms can also be badly biased, but the bias can be much reduced if separate budgets are maintained for the A and B arms of the experiment.
BibTeX
@InProceedings{pmlr-v51-basse16b,
title = {Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments},
author = {Basse, Guillaume W. and Azari Soufiani, Hossein and Lambert, Diane},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {1412--1420},
year = {2016},
editor = {Gretton, Arthur and Robert, Christian C.},
volume = {51},
series = {Proceedings of Machine Learning Research},
address = {Cadiz, Spain},
month = {09--11 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v51/basse16b.pdf},
url = {https://proceedings.mlr.press/v51/basse16b.html},
abstract = {Buyers (e.g., advertisers) often have limited financial or processing resources, and so their participation in auctions is throttled. Changes to auctions may affect bids or throttling, and any change may affect what buyers pay. This paper shows that if an A/B experiment affects only bids, then the observed treatment effect is an unbiased estimator when all the bidders in the same auction are randomly assigned to A or B but the observed treatment effect can be severely biased otherwise, even in the absence of throttling. Experiments that affect throttling algorithms can also be badly biased, but the bias can be much reduced if separate budgets are maintained for the A and B arms of the experiment.}
}