Workshop in Applied Statistics


Wednesday, September 23, 2020, 12:00pm to 1:30pm


Zoom - see below

You can attend this workshop via this link:

If you would like to be added to the email list to receive reminders and information about the series, please send your email address to Soichiro Yamauchi (

Today's presenter

Reagan Mozer (Bentley University), "Recent Adventures in Causal(ish) Inference with Text as Data"


Text data have a long history in social science and education research. However, these data are notoriously high-dimensional and characterized by many nuances of language that lack plausible statistical models. As a result, analysis of text data typically involves intensive human coding tasks where particular constructs or features of the text are first defined, and then a collection of documents are inspected and coded for the presence or absence of these constructs. While this process may be feasible in studies with smaller sample sizes, the time and resources required to train and employ multiple human coders frequently poses a challenge for large-scale efforts. In this talk, I will consider how to reliably and efficiently extract meaningful constructs from text documents in a manner that preserves human judgment, primarily for the purposes of supporting causal inferences in randomized where some outcomes of interest are features of text generated by the trial’s participants. To illustrate how text data might be leveraged in various inferential settings both in and out of the causal realm, I will present results from three recent studies in education, medicine, and public health.

The Applied Statistics Workshop (Gov 3009) is a forum for graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers. It is co-hosted by Harvard University's Department of Government and Institute for Quantitative Social Science (IQSS).

For more information, visit the Applied Statistics website.