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

In many cases, a machine learning model must learn to correctly predict a few
data points with particular values of interest in a broader range of data where
many target values are zero. Zero-inflated data can be found in diverse
scenarios, such as lumpy and intermittent demands, power consumption for home
appliances being turned on and off, impurities measurement in distillation
processes, and even airport shuttle demand prediction. The presence of zeroes
affects the models' learning and may result in poor performance. Furthermore,
zeroes also distort the metrics used to compute the model's prediction quality.
This paper showcases two real-world use cases (home appliances classification
and airport shuttle demand prediction) where a hierarchical model applied in
the context of zero-inflated data leads to excellent results. In particular,
for home appliances classification, the weighted average of Precision, Recall,
F1, and AUC ROC was increased by 27%, 34%, 49%, and 27%, respectively.
Furthermore, it is estimated that the proposed approach is also four times more
energy efficient than the SOTA approach against which it was compared to.
Two-fold models performed best in all cases when predicting airport shuttle
demand, and the difference against other models has been proven to be
statistically significant.

这篇论文介绍了机器学习模型在零膨胀数据环境中的应用，展示了在家用电器分类和机场班车需求预测两个实际应用中，采用层次模型在预测性能和能效方面取得的优异结果。