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 …

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

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 …

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 …

Hyperparameters critically influence how well machine learning models perform on unseen,
out-of-sample data. Systematically comparing the performance of different hyperparameter …

Text as data techniques offer a great promise: the ability to inductively discover measures
that are useful for testing social science theories with large collections of text. Nearly all text …

We identify situations in which conditioning on text can address confounding in
observational studies. We argue that a matching approach is particularly well‐suited to this …

While texts are the primary carriers of information for government decision making, few
studies have examined the role of textual complexity in government-citizen communication …

We present novel evidence on the effects of advanced technologies on employment, skill
demand, and firm performance. The main finding is that advanced technologies led to …

Randomized clinical trials (RCT) are the gold standard for informing treatment decisions.
Observational studies are often plagued by selection bias, and expert-selected covariates …