  New text as data techniques offer a great promise: the ability to inductively
discover measures that are useful for testing social science theories of
interest from large collections of text. We introduce a conceptual framework
for making causal inferences with discovered measures as a treatment or
outcome. Our framework enables researchers to discover high-dimensional textual
interventions and estimate the ways that observed treatments affect text-based
outcomes. We argue that nearly all text-based causal inferences depend upon a
latent representation of the text and we provide a framework to learn the
latent representation. But estimating this latent representation, we show,
creates new risks: we may introduce an identification problem or overfit. To
address these risks we describe a split-sample framework and apply it to
estimate causal effects from an experiment on immigration attitudes and a study
on bureaucratic response. Our work provides a rigorous foundation for
text-based causal inferences.
