The vast majority of existing methods and systems for causal inference assume
that all variables under consideration are categorical or numerical (e.g.,
gender, price, blood pressure, enrollment). In this paper, we present
CausalNLP, a toolkit for inferring causality from observational data that
includes text in addition to traditional numerical and categorical variables.
CausalNLP employs the use of meta-learners for treatment effect estimation and
supports using raw text and its linguistic properties as both a treatment and a
"controlled-for" variable (e.g., confounder). The library is open-source and
available at: https://github.com/amaiya/causalnlp.Comment: 7 page