  Many applications of computational social science aim to infer causal
conclusions from non-experimental data. Such observational data often contains
confounders, variables that influence both potential causes and potential
effects. Unmeasured or latent confounders can bias causal estimates, and this
has motivated interest in measuring potential confounders from observed text.
For example, an individual's entire history of social media posts or the
content of a news article could provide a rich measurement of multiple
confounders. Yet, methods and applications for this problem are scattered
across different communities and evaluation practices are inconsistent. This
review is the first to gather and categorize these examples and provide a guide
to data-processing and evaluation decisions. Despite increased attention on
adjusting for confounding using text, there are still many open problems, which
we highlight in this paper.
