This manuscript is technical memoir about my work at Stanford and Microsoft
Research. Included are fundamental concepts central to machine learning and
artificial intelligence, applications of these concepts, and stories behind
their creation.

本文是关于机器学习和人工智能基本概念、应用和创作故事的技术回忆录。

Heckerthoughts

We examine the role of textual data as study units when conducting causal
inference by drawing parallels between human subjects and organized texts. %in
human population research. We elaborate on key causal concepts and principles,
and expose some ambiguity and sometimes fallacies. To facilitate better framing
a causal query, we discuss two strategies: (i) shifting from immutable traits
to perceptions of them, and (ii) shifting from some abstract concept/property
to its constituent parts, i.e., adopting a constructivist perspective of an
abstract concept. We hope this article would raise the awareness of the
importance of articulating and clarifying fundamental concepts before delving
into developing methodologies when drawing causal inference using textual data.

通过绘制人类主体和有组织文本之间的平行关系，我们探讨了文本数据在进行因果推论时的作用。文章强调了关键因果概念和原则，并提出了两种策略来更好地确定因果查询问题。我们希望本文能提高人们对在使用文本数据进行因果推论时阐述和澄清基本概念的重要性的认识。