1 citations · 1 across the 5 of their papers we have counts for
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The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text
Marie Neubrander, Graham Tierney, Alexander Volfovsky
Estimating causal effects of linguistic properties from observational text is difficult because the same document can contain both the treatment of interest and the non-treatment t…
A Design-based Solution for Causal Inference with Text: Can a Language Model Be Too Large?
Graham Tierney, Srikar Katta, Christopher Bail +2
Many social science questions ask how linguistic properties causally affect an audience's attitudes and behaviors. Because text properties are often interlinked (e.g., angry review…
Compositional dynamic modelling for causal prediction in multivariate time series
Kevin Li, Graham Tierney, Christoph Hellmayr +1
Theoretical developments in sequential Bayesian analysis of multivariate dynamic models underlie new methodology for causal prediction. This extends the utility of existing models…
Reply to Discussions of "Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue"
Anna K. Yanchenko, Graham Tierney, Joseph Lawson +3
We are most grateful to all discussants for their positive comments and many thought-provoking questions. In addition, the discussants provide a number of useful leads into various…
Multivariate Bayesian dynamic modeling for causal prediction
Graham Tierney, Christoph Hellmayr, Greg Barkimer +2
Bayesian forecasting is developed in multivariate time series analysis for causal inference. Causal evaluation of sequentially observed time series data from control and treated un…