HCI researchers' and practitioners' awareness of intersectionality has been
expanding, producing knowledge, recommendations, and prototypes for supporting
intersectional populations. However, doing intersectional HCI work is uniquely
expensive: it leads to a combinatorial explosion of empirical work (expense 1),
and little of the work on one intersectional population can be leveraged to
serve another (expense 2). In this paper, we explain how representations
employed by certain analytical design methods correspond to type abstractions,
and use that correspondence to identify a (de)compositional model in which a
population's diverse identity properties can be joined and split. We formally
prove the model's correctness, and show how it enables HCI designers to harness
existing analytical HCI methods for use on new intersectional populations of
interest. We illustrate through four design use-cases, how the model can reduce
the amount of expense 1 and enable designers to leverage prior work to new
intersectional populations, addressing expense 2.

本文介绍了一种分析性设计方法来帮助 HCI 设计师将一个人群的多种身份属性连接和分离以解决研究难度大和先前工作无法转化的问题，并通过四个设计用例说明了该模型如何降低研究成本和为新的交叉人群提供帮助的作用。

交叉性分析：通过类型抽象控制组合爆炸

Intersectionality Goes Analytical: Taming Combinatorial Explosion Through Type Abstraction

In this essay I discuss potential outcome and graphical approaches to
causality, and their relevance for empirical work in economics. I review some
of the work on directed acyclic graphs, including the recent "The Book of Why,"
by Pearl and MacKenzie. I also discuss the potential outcome framework
developed by Rubin and coauthors, building on work by Neyman. I then discuss
the relative merits of these approaches for empirical work in economics,
focusing on the questions each answer well, and why much of the the work in
economics is closer in spirit to the potential outcome framework.

本文讨论了因果关系的潜在结果和图形方法，回顾了一些关于有向无环图的工作以及潜在结果框架的相对价值，着重探讨了在经济学的实证研究中这些方法的优点和缺点，以及为什么经济学的大部分工作与潜在结果框架更为接近。