CausalNLP: A Practical Toolkit for Causal Inference with Text
It addresses a gap for researchers and practitioners in causal inference by extending methods to handle text data, though it is incremental as it adapts existing meta learners to this context.
The paper tackles the problem of causal inference with observational data that includes text variables, presenting CausalNLP as a toolkit that uses meta learners to estimate treatment effects with text as treatment, outcome, or confounder.
Causal inference is the process of estimating the effect or impact of a treatment on an outcome with other covariates as potential confounders (and mediators) that may need to be controlled. 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, enrollment). In this paper, we present CausalNLP, a toolkit for inferring causality with 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 a treatment, an outcome, or a "controlled-for" variable (e.g., confounder). The library is open source and available at: https://github.com/amaiya/causalnlp.