Comparing two samples of data, we observe a change in the distribution of an
outcome variable. In the presence of multiple explanatory variables, how much
of the change can be explained by each possible cause? We develop a new
estimation strategy that, given a causal model, combines regression and
re-weighting methods to quantify the contribution of each causal mechanism. Our
proposed methodology is multiply robust, meaning that it still recovers the
target parameter under partial misspecification. We prove that our estimator is
consistent and asymptotically normal. Moreover, it can be incorporated into
existing frameworks for causal attribution, such as Shapley values, which will
inherit the consistency and large-sample distribution properties. Our method
demonstrates excellent performance in Monte Carlo simulations, and we show its
usefulness in an empirical application.

比较两个数据样本，我们观察到一个结果变量分布的变化。在存在多个解释变量的情况下，我们开发了一种新的估计策略，结合回归和重新加权方法来量化每个因果机制的贡献。我们的提出方法是多重健壮的，意味着在部分错误规范化下仍能恢复目标参数。我们证明了我们的估计量是一致和渐进正态的。此外，它可以被纳入到 Shapley 值等现有的因果归因框架中，从而继承其一致性和大样本分布属性。我们的方法在蒙特卡洛模拟中表现出色，并且我们展示了其在实证应用中的实用性。

多重稳健的因果变化归因

Multiply-Robust Causal Change Attribution

Time series of counts arise in a variety of forecasting applications, for
which traditional models are generally inappropriate. This paper introduces a
hierarchical Bayesian formulation applicable to count time series that can
easily account for explanatory variables and share statistical strength across
groups of related time series. We derive an efficient approximate inference
technique, and illustrate its performance on a number of datasets from supply
chain planning.

本文介绍了一种适用于计数时间序列数据的分层贝叶斯模型，能够轻松地考虑影响因素和在相关的时间序列数据组之间共享统计信息，并提出了一种高效的近似推理方法，并在供应链规划的多个数据集上进行了性能测试。