A common type of zero-inflated data has certain true values incorrectly
replaced by zeros due to data recording conventions (rare outcomes assumed to
be absent) or details of data recording equipment (e.g. artificial zeros in
gene expression data).
Existing methods for zero-inflated data either fit the observed data
likelihood via parametric mixture models that explicitly represent excess
zeros, or aim to replace excess zeros by imputed values. If the goal of the
analysis relies on knowing true data realizations, a particular challenge with
zero-inflated data is identifiability, since it is difficult to correctly
determine which observed zeros are real and which are inflated.
This paper views zero-inflated data as a general type of missing data
problem, where the observability indicator for a potentially censored variable
is itself unobserved whenever a zero is recorded. We show that, without
additional assumptions, target parameters involving a zero-inflated variable
are not identified. However, if a proxy of the missingness indicator is
observed, a modification of the effect restoration approach of Kuroki and Pearl
allows identification and estimation, given the proxy-indicator relationship is
known.
If this relationship is unknown, our approach yields a partial identification
strategy for sensitivity analysis. Specifically, we show that only certain
proxy-indicator relationships are compatible with the observed data
distribution. We give an analytic bound for this relationship in cases with a
categorical outcome, which is sharp in certain models. For more complex cases,
sharp numerical bounds may be computed using methods in Duarte et al.[2023].
We illustrate our method via simulation studies and a data application on
central line-associated bloodstream infections (CLABSIs).

将零膨胀数据视为一种普遍类型的缺失数据问题，通过观察到的缺失指示器来实现特定参数的识别和估计，未知关系时采用部分识别策略进行敏感性分析。

零通胀作为缺失数据问题：基于代理方法的解决方案

Zero Inflation as a Missing Data Problem: a Proxy-based Approach

Machine learning techniques can be useful in applications such as credit
approval and college admission. However, to be classified more favorably in
such contexts, an agent may decide to strategically withhold some of her
features, such as bad test scores. This is a missing data problem with a twist:
which data is missing {\em depends on the chosen classifier}, because the
specific classifier is what may create the incentive to withhold certain
feature values. We address the problem of training classifiers that are robust
to this behavior.
We design three classification methods: {\sc Mincut}, {\sc Hill-Climbing}
({\sc HC}) and Incentive-Compatible Logistic Regression ({\sc IC-LR}). We show
that {\sc Mincut} is optimal when the true distribution of data is fully known.
However, it can produce complex decision boundaries, and hence be prone to
overfitting in some cases. Based on a characterization of truthful classifiers
(i.e., those that give no incentive to strategically hide features), we devise
a simpler alternative called {\sc HC} which consists of a hierarchical ensemble
of out-of-the-box classifiers, trained using a specialized hill-climbing
procedure which we show to be convergent. For several reasons, {\sc Mincut} and
{\sc HC} are not effective in utilizing a large number of complementarily
informative features. To this end, we present {\sc IC-LR}, a modification of
Logistic Regression that removes the incentive to strategically drop features.
We also show that our algorithms perform well in experiments on real-world data
sets, and present insights into their relative performance in different
settings.

本研究旨在解决机器学习中的数据缺失问题。我们设计了三种分类方法：Mincut、Hill-Climbing 和 Incentive-Compatible Logistic Regression。我们的研究表明，这些算法在真实数据集上表现良好且具有一定的实用性。

带有策略性保留数据的分类

Classification with Strategically Withheld Data

This paper presents a method of zero-shot learning (ZSL) which poses ZSL as
the missing data problem, rather than the missing label problem. Specifically,
most existing ZSL methods focus on learning mapping functions from the image
feature space to the label embedding space. Whereas, the proposed method
explores a simple yet effective transductive framework in the reverse way \---
our method estimates data distribution of unseen classes in the image feature
space by transferring knowledge from the label embedding space. In experiments,
our method outperforms the state-of-the-art on two popular datasets.

本文探讨了零样本学习方法在解决 ' 缺失数据问题 ' 而非 ' 缺失标签问题 ' 时的有效性，通过将知识从标签嵌入空间转移到图像特征空间，估计未见过的类在图像特征空间的数据分布，实验表明，与现有方法相比，该方法在两个流行数据集上的表现更优