Prediction becomes more challenging with missing covariates. What method is
chosen to handle missingness can greatly affect how models perform. In many
real-world problems, the best prediction performance is achieved by models that
can leverage the informative nature of a value being missing. Yet, the reasons
why a covariate goes missing can change once a model is deployed in practice.
If such a missingness shift occurs, the conditional probability of a value
being missing differs in the target data. Prediction performance in the source
data may no longer be a good selection criterion, and approaches that do not
rely on informative missingness may be preferable. However, we show that the
Bayes predictor remains unchanged by ignorable shifts for which the probability
of missingness only depends on observed data. Any consistent estimator of the
Bayes predictor may therefore result in robust prediction under those
conditions, although we show empirically that different methods appear robust
to different types of shifts. If the missingness shift is non-ignorable, the
Bayes predictor may change due to the shift. While neither approach recovers
the Bayes predictor in this case, we found empirically that disregarding
missingness was most beneficial when it was highly informative.

预测表现受到缺失协变量的影响，处理缺失数据的方法选择会极大地影响模型性能。研究发现，具有信息性缺失值的模型可以实现最佳的预测性能，但实际部署后缺失值产生的原因可能会发生变化，此时基于缺失的条件概率在目标数据中会有所不同。因此，如果缺失值的产生是不可忽略的，Bayes 预测器可能会因为缺失值的变化而有所改变。然而，我们的实证研究发现，在高度信息化的情况下，忽略缺失值是最有益的。

缺失偏移下的稳健预测

Robust prediction under missingness shifts

Multivariate time series data in practical applications, such as health care,
geoscience, and biology, are characterized by a variety of missing values. In
time series prediction and other related tasks, it has been noted that missing
values and their missing patterns are often correlated with the target labels,
a.k.a., informative missingness. There is very limited work on exploiting the
missing patterns for effective imputation and improving prediction performance.
In this paper, we develop novel deep learning models, namely GRU-D, as one of
the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a
state-of-the-art recurrent neural network. It takes two representations of
missing patterns, i.e., masking and time interval, and effectively incorporates
them into a deep model architecture so that it not only captures the long-term
temporal dependencies in time series, but also utilizes the missing patterns to
achieve better prediction results. Experiments of time series classification
tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic
datasets demonstrate that our models achieve state-of-the-art performance and
provides useful insights for better understanding and utilization of missing
values in time series analysis.

本研究提出一种基于 GRU 的深度学习模型 GRU-D，利用缺失模式中的缺失值及其时间间隔，成功地捕获时间序列中的长期时间依赖关系，提高了时间序列分类任务的预测性能，并为缺失值在时间序列分析中的更好理解和利用提供了有用的见解。