[gov3009-l] 10/4 Applied Stats: Molly Roberts

Dana Higgins danahiggins at fas.harvard.edu
Mon Oct 2 13:05:38 EDT 2017


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Molly
Roberts*, Assistant Professor at the University of California, San Diego.
She will be presenting work entitled *How to Make Causal Inferences Using
Text*.  Please find the abstract below and on the Applied Stats website here
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>.



As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided.  See you all there!



-- Dana Higgins




*Title:* *How to Make Causal Inferences Using Text*
(with Naoki Egami, Christian Fong, Justin Grimmer and Brandon Stewart)


*Abstract:* Texts are increasingly used to make causal inferences: either
with the document serving as the treatment or the outcome.  We introduce a
new conceptual framework to understand all text-based causal inferences,
demonstrate fundamental problems that arise when using manual or
computational approaches applied to text for causal inference, and provide
solutions to the problems we raise.  We demonstrate that all text-based
causal inferences depend upon a latent representation of the text and we
provide a framework to learn the latent representation. Estimating this
latent representation, however, creates new risks: we may unintentionally
create a dependency across observations or create opportunities to fish for
large effects.  To address these risks, we introduce a train/test split
framework and apply it to estimate causal effects from an experiment on
immigration attitudes and a study on bureaucratic responsiveness.  Our work
provides a rigorous foundation for text-based causal inferences, connecting
two previous disparate literatures.
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