In this study, we explore the impact of different masking strategies on time
series imputation models. We evaluate the effects of pre-masking versus
in-mini-batch masking, normalization timing, and the choice between augmenting
and overlaying artificial missingness. Using three diverse datasets, we
benchmark eleven imputation models with different missing rates. Our results
demonstrate that masking strategies significantly influence imputation
accuracy, revealing that more sophisticated and data-driven masking designs are
essential for robust model evaluation. We advocate for refined experimental
designs and comprehensive disclosureto better simulate real-world patterns,
enhancing the practical applicability of imputation models.

本研究探讨不同的遮蔽策略对时间序列插补模型的影响，评估了预遮蔽与 mini-batch 中的遮蔽、标准化时机以及增强人工缺失和叠加人工缺失之间的选择对插补准确性的影响。使用三个不同的数据集，我们对 11 个插补模型进行了基准测试，其中包含不同的缺失率。我们的结果表明，遮蔽策略显著影响插补准确性，揭示了更复杂和数据驱动的遮蔽设计对于稳健的模型评估至关重要。我们倡导改进的实验设计和全面披露，以更好地模拟现实世界的模式，增强插补模型的实际适用性。

解开秘密：掩盖策略如何塑造时间序列插补

Unveiling the Secrets: How Masking Strategies Shape Time Series  Imputation

Treatment effect estimation is a fundamental problem in causal inference. We
focus on designing efficient randomized controlled trials, to accurately
estimate the effect of some treatment on a population of $n$ individuals. In
particular, we study sample-constrained treatment effect estimation, where we
must select a subset of $s \ll n$ individuals from the population to experiment
on. This subset must be further partitioned into treatment and control groups.
Algorithms for partitioning the entire population into treatment and control
groups, or for choosing a single representative subset, have been well-studied.
The key challenge in our setting is jointly choosing a representative subset
and a partition for that set.
We focus on both individual and average treatment effect estimation, under a
linear effects model. We give provably efficient experimental designs and
corresponding estimators, by identifying connections to discrepancy
minimization and leverage-score-based sampling used in randomized numerical
linear algebra. Our theoretical results obtain a smooth transition to known
guarantees when $s$ equals the population size. We also empirically demonstrate
the performance of our algorithms.

本研究关注样本受限制条件下的治疗效果估计问题，基于线性效应模型设计了有效的实验方案和估算器，涉及随机控制试验、子集选择、划分以及采样等方法。

受限治疗效应估计样例

Sample Constrained Treatment Effect Estimation

Online experiments are widely used to compare specific design alternatives,
but they can also be used to produce generalizable knowledge and inform
strategic decision making. Doing so often requires sophisticated experimental
designs, iterative refinement, and careful logging and analysis. Few tools
exist that support these needs. We thus introduce a language for online field
experiments called PlanOut. PlanOut separates experimental design from
application code, allowing the experimenter to concisely describe experimental
designs, whether common "A/B tests" and factorial designs, or more complex
designs involving conditional logic or multiple experimental units. These
latter designs are often useful for understanding causal mechanisms involved in
user behaviors. We demonstrate how experiments from the literature can be
implemented in PlanOut, and describe two large field experiments conducted on
Facebook with PlanOut. For common scenarios in which experiments are run
iteratively and in parallel, we introduce a namespaced management system that
encourages sound experimental practice.

介绍了一种用于在线实验的语言 PlanOut，支持常见和更复杂的实验设计，可用于理解用户行为的因果机制，通过两个在 Facebook 上进行的大型实验展示其应用。