Standard methods in preference learning involve estimating the parameters of
discrete choice models from data of selections (choices) made by individuals
from a discrete set of alternatives (the choice set). While there are many
models for individual preferences, existing learning methods overlook how
choice set assignment affects the data. Often, the choice set itself is
influenced by an individual's preferences; for instance, a consumer choosing a
product from an online retailer is often presented with options from a
recommender system that depend on information about the consumer's preferences.
Ignoring these assignment mechanisms can mislead choice models into making
biased estimates of preferences, a phenomenon that we call choice set
confounding; we demonstrate the presence of such confounding in widely-used
choice datasets.
To address this issue, we adapt methods from causal inference to the discrete
choice setting. We use covariates of the chooser for inverse probability
weighting and/or regression controls, accurately recovering individual
preferences in the presence of choice set confounding under certain
assumptions. When such covariates are unavailable or inadequate, we develop
methods that take advantage of structured choice set assignment to improve
prediction. We demonstrate the effectiveness of our methods on real-world
choice data, showing, for example, that accounting for choice set confounding
makes choices observed in hotel booking and commute transportation more
consistent with rational utility-maximization.

通过因果推断的方法适应离散选择设置，使用选择者的协变量进行反向概率加权和 / 或回归控制，以便在一定假设下准确地恢复个体偏好。当这些协变量不可用或不充分时，使用结构化选择集分配的方法来提高预测准确性，并在真实世界的选择数据上展示了方法的有效性，从而使在酒店预订和通勤交通等情况下发现的选择与理性的效用最大化更加一致。

离散选择中的选择集混杂

Choice Set Confounding in Discrete Choice

In this paper, we study the dynamic assortment optimization problem under a
finite selling season of length $T$. At each time period, the seller offers an
arriving customer an assortment of substitutable products under a cardinality
constraint, and the customer makes the purchase among offered products
according to a discrete choice model. Most existing work associates each
product with a real-valued fixed mean utility and assumes a multinomial logit
choice (MNL) model. In many practical applications, feature/contexutal
information of products is readily available. In this paper, we incorporate the
feature information by assuming a linear relationship between the mean utility
and the feature. In addition, we allow the feature information of products to
change over time so that the underlying choice model can also be
non-stationary. To solve the dynamic assortment optimization under this
changing contextual MNL model, we need to simultaneously learn the underlying
unknown coefficient and makes the decision on the assortment. To this end, we
develop an upper confidence bound (UCB) based policy and establish the regret
bound on the order of $\widetilde O(d\sqrt{T})$, where $d$ is the dimension of
the feature and $\widetilde O$ suppresses logarithmic dependence. We further
established the lower bound $\Omega(d\sqrt{T}/K)$ where $K$ is the cardinality
constraint of an offered assortment, which is usually small. When $K$ is a
constant, our policy is optimal up to logarithmic factors. In the exploitation
phase of the UCB algorithm, we need to solve a combinatorial optimization for
assortment optimization based on the learned information. We further develop an
approximation algorithm and an efficient greedy heuristic. The effectiveness of
the proposed policy is further demonstrated by our numerical studies.

本文研究了长度为 T 的有限销售季节下的动态组合优化问题，其中包括了具有属性信息的产品的线性关系和上置信界 (UCB) 算法，并使用组合优化法进行了解决。

动态情境信息下的产品组合优化

Dynamic Assortment Optimization with Changing Contextual Information

Nonmonotone missing data arise routinely in empirical studies of social and
health sciences, and when ignored, can induce selection bias and loss of
efficiency. In practice, it is common to account for nonresponse under a
missing-at-random assumption which although convenient, is rarely appropriate
when nonresponse is nonmonotone. Likelihood and Bayesian missing data
methodologies often require specification of a parametric model for the full
data law, thus a priori ruling out any prospect for semiparametric inference.
In this paper, we propose an all-purpose approach which delivers semiparametric
inferences when missing data are nonmonotone and not at random. The approach is
based on a discrete choice model (DCM) as a means to generate a large class of
nonmonotone nonresponse mechanisms that are nonignorable. Sufficient conditions
for nonparametric identification are given, and a general framework for fully
parametric and semiparametric inference under an arbitrary DCM is proposed.
Special consideration is given to the case of logit discrete choice nonresponse
model (LDCM) for which we describe generalizations of inverse-probability
weighting, pattern-mixture estimation, doubly robust estimation and multiply
robust estimation.

本文提出了一种基于离散选择模型的全能法，可以在缺失数据为非单调且不随机的情况下提供半参数推断，并针对逻辑斯谛离散选择不响应模型（LDCM）进行了特别考虑。