We focus on the task of reasoning over paragraph effects in situation, which
requires a model to understand the cause and effect described in a background
paragraph, and apply the knowledge to a novel situation. Existing works ignore
the complicated reasoning process and solve it with a one-step "black box"
model. Inspired by human cognitive processes, in this paper we propose a
sequential approach for this task which explicitly models each step of the
reasoning process with neural network modules. In particular, five reasoning
modules are designed and learned in an end-to-end manner, which leads to a more
interpretable model. Experimental results on the ROPES dataset demonstrate the
effectiveness and explainability of our proposed approach.

本文探讨了一种基于神经网络的逐步推理模型，旨在更细致地理解为背景段落中叙述的因果关系，并将此知识应用到新的情境中，实验结果表明了其有效性和可解释性。

面向情境中段落影响力的可解释性推理

Towards Interpretable Reasoning over Paragraph Effects in Situation

Book review published as: Aronow, Peter M. and Fredrik S\"avje (2020), "The
Book of Why: The New Science of Cause and Effect." Journal of the American
Statistical Association, 115: 482-485.

本文是一篇《美国统计协会杂志》上发表的书评，对《The Book of Why: The New Science of Cause and Effect》一书进行了评述。

「为什么」之书评：因果关系的新科学

Review of The Book of Why: The New Science of Cause and Effect

Inferring the causal structure of a set of random variables from a finite
sample of the joint distribution is an important problem in science. Recently,
methods using additive noise models have been suggested to approach the case of
continuous variables. In many situations, however, the variables of interest
are discrete or even have only finitely many states. In this work we extend the
notion of additive noise models to these cases. We prove that whenever the
joint distribution $\prob^{(X,Y)}$ admits such a model in one direction, e.g.
$Y=f(X)+N, N \independent X$, it does not admit the reversed model
$X=g(Y)+\tilde N, \tilde N \independent Y$ as long as the model is chosen in a
generic way. Based on these deliberations we propose an efficient new algorithm
that is able to distinguish between cause and effect for a finite sample of
discrete variables. In an extensive experimental study we show that this
algorithm works both on synthetic and real data sets.

本研究基于加性噪声模型，扩展了因果推论在离散变量情况下的应用，提出了一种快速的算法，能够有效判断有限离散变量的因果关系，经实验验证表明该算法可行。