Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

Social scientists are now in an era of data abundance, and machine learning tools are
increasingly used to extract meaning from data sets both massive and small. We explain …

A fundamental goal of scientific research is to learn about causal relationships. However,
despite its critical role in the life and social sciences, causality has not had the same …

This paper provides an assessment of the early contributions of machine learning to
economics, as well as predictions about its future contributions. It begins by briefly …

We make only one point in this article. Every quantitative study must be able to answer the
question: what is your estimand? The estimand is the target quantity—the purpose of the …

Understanding predictions made by deep neural networks is notoriously difficult, but also
crucial to their dissemination. As all machine learning–based methods, they are as good as …

A guide for using computational text analysis to learn about the social world From social
media posts and text messages to digital government documents and archives, researchers …

Many applications of computational social science aim to infer causal conclusions from non-
experimental data. Such observational data often contains confounders, variables that …

Controllable text generation concerns two fundamental tasks of wide applications, namely
generating text of given attributes (ie, attribute-conditional generation), and minimally editing …

Does adding a theorem to a paper affect its chance of acceptance? Does labeling a post
with the author's gender affect the post popularity? This paper develops a method to …