Causal inference from text: A commentary

  title={Causal inference from text: A commentary},
  author={Dhanya Sridhar and David M. Blei},
  journal={Science Advances},
Statistical and machine learning methods help social scientists and other researchers make causal inferences from texts. 



How to make causal inferences using texts

A split-sample workflow for making rigorous causal inferences with discovered measures as treatments or outcomes is introduced and applied to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.

Structural Topic Models for Open‐Ended Survey Responses

The structural topic model makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects, and is illustrated with analysis of text from surveys and experiments.

When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications

The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan.

Causal Inference with Latent Treatments