What assortments (subsets of items) should be offered, to collect data for estimating a choice model over $n$ total items? We propose a structured, non-adaptive experiment design requiring only $O(\log n)$ distinct assortments, each offered repeatedly, that consistently outperforms randomized and other heuristic designs across an extensive numerical benchmark that estimates multiple different choice models under a variety of (possibly mis-specified) ground truths. We then focus on Nested Logit choice models, which cluster items into "nests" of close substitutes. Whereas existing Nested Logit estimation procedures assume the nests to be known and fixed, we present a new algorithm to identify nests based on collected data, which when used in conjunction with our experiment design, guarantees correct identification of nests under any Nested Logit ground truth. Our experiment design was deployed to collect data from over 70 million users at Dream11, an Indian fantasy sports platform that offers different types of betting contests, with rich substitution patterns between them. We identify nests based on the collected data, which lead to better out-of-sample choice prediction than ex-ante clustering from contest features. Our identified nests are ex-post justifiable to Dream11 management.

翻译：为估计在$n$个总商品上的选择模型，应提供何种商品组合（商品的子集）以收集数据？我们提出了一种结构化、非自适应的实验设计，仅需$O(\log n)$个不同的商品组合（每个组合被重复提供），在广泛的数值基准测试中，该设计在估计多种不同选择模型（基于各种可能误设的真实情况）时，始终优于随机化及其他启发式设计。随后，我们聚焦于嵌套Logit选择模型，该模型将商品聚类为紧密替代品的“嵌套”。现有的嵌套Logit估计程序假定嵌套已知且固定，而我们提出了一种基于收集数据识别嵌套的新算法。当该算法与我们的实验设计结合使用时，可保证在任何嵌套Logit真实情况下正确识别嵌套。我们的实验设计已在Dream11（一家印度梦幻体育平台，提供不同类型的投注竞赛，竞赛间存在丰富的替代模式）部署，用于收集超过7000万用户的数据。我们基于收集的数据识别出嵌套，相比基于竞赛特征的先验聚类，这些嵌套带来了更好的样本外选择预测效果。我们识别出的嵌套在事后可向Dream11管理层提供合理解释。