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

Few-shot learning with N-way K-shot scheme is an open challenge in machine
learning. Many approaches have been proposed to tackle this problem, e.g., the
Matching Networks and CLIP-Adapter. Despite that these approaches have shown
significant progress, the mechanism of why these methods succeed has not been
well explored. In this paper, we interpret these few-shot learning methods via
causal mechanism. We show that the existing approaches can be viewed as
specific forms of front-door adjustment, which is to remove the effects of
confounders. Based on this, we introduce a general causal method for few-shot
learning, which considers not only the relationship between examples but also
the diversity of representations. Experimental results demonstrate the
superiority of our proposed method in few-shot classification on various
benchmark datasets. Code is available in the supplementary material.

本文通过因果机制解释了少样本学习方法，提出了一种在考虑示例之间关系和表征多样性的基于因果的少样本学习方法，并在各种基准数据集上证明了方法的卓越性能。

从因果分析的角度重新审视少样本学习

Revisiting Few-Shot Learning from a Causal Perspective

We consider decision problems under uncertainty where the options available
to a decision maker and the resulting outcome are related through a causal
mechanism which is unknown to the decision maker. We ask how a decision maker
can learn about this causal mechanism through sequential decision making as
well as using current causal knowledge inside each round in order to make
better choices had she not considered causal knowledge and propose a decision
making procedure in which an agent holds \textit{beliefs} about her environment
which are used to make a choice and are updated using the observed outcome. As
proof of concept, we present an implementation of this causal decision making
model and apply it in a simple scenario. We show that the model achieves a
performance similar to the classic Q-learning while it also acquires a causal
model of the environment.

研究决策过程中的不确定性环境，设计一种决策模型以帮助决策者更好地理解因果机制，从而通过观察结果不断优化决策并逐步建立起因果模型