Estimating average causal effects is a common practice to test new
treatments. However, the average effect ''masks'' important individual
characteristics in the counterfactual distribution, which may lead to safety,
fairness, and ethical concerns. This issue is exacerbated in the temporal
setting, where the treatment is sequential and time-varying, leading to an
intricate influence on the counterfactual distribution. In this paper, we
propose a novel conditional generative modeling approach to capture the whole
counterfactual distribution, allowing efficient inference on certain statistics
of the counterfactual distribution. This makes the proposed approach
particularly suitable for healthcare and public policy making. Our generative
modeling approach carefully tackles the distribution mismatch in the observed
data and the targeted counterfactual distribution via a marginal structural
model. Our method outperforms state-of-the-art baselines on both synthetic and
real data.

本文提出了一种基于条件生成建模的方法，旨在捕捉整个反事实分布，使其特别适用于医疗保健和公共政策制定。通过边际结构模型来解决观察数据和目标反事实分布之间的分布不匹配问题，该方法在合成和真实数据上优于现有方法。

时间变化治疗的反事实生成模型

Counterfactual Generative Models for Time-Varying Treatments

When the performance of a machine learning model varies over groups defined
by sensitive attributes (e.g., gender or ethnicity), the performance disparity
can be expressed in terms of the probability distributions of the input and
output variables over each group. In this paper, we exploit this fact to reduce
the disparate impact of a fixed classification model over a population of
interest. Given a black-box classifier, we aim to eliminate the performance gap
by perturbing the distribution of input variables for the disadvantaged group.
We refer to the perturbed distribution as a counterfactual distribution, and
characterize its properties for common fairness criteria. We introduce a
descent algorithm to learn a counterfactual distribution from data. We then
discuss how the estimated distribution can be used to build a data preprocessor
that can reduce disparate impact without training a new model. We validate our
approach through experiments on real-world datasets, showing that it can repair
different forms of disparity without a significant drop in accuracy.

利用机器学习模型的输出和输入的概率分布对基于敏感属性（如性别或种族）的性能不均衡进行量化，并通过扰动劣势群体的输入变量的分布来减少固定分类模型对感兴趣人群的影响，从而通过数据预处理器实现消除性能差异的目的。