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Causal inference is the process of estimating the effect or impact of atreatment on an outcome with other covariates as potential confounders (andmediators) that may need to be controlled. The vast majority of existingmethods and systems for causal inference assume that all variables underconsideration are categorical or numerical (e.g., gender, price, enrollment).In this paper, we present CausalNLP, a toolkit for inferring causality withobservational data that includes text in addition to traditional numerical andcategorical variables. CausalNLP employs the use of meta learners for treatmenteffect estimation and supports using raw text and its linguistic properties asa treatment, an outcome, or a "controlled-for" variable (e.g., confounder). Thelibrary is open source and available at: https://github.com/amaiya/causalnlp.

CausalNLP: A Practical Toolkit for Causal Inference with Text