CausalNLP：一个文本因果推理实用工具包
CausalNLP: A Practical Toolkit for Causal Inference with Text
摘要：
因果推断是估计治疗对结果的效果或影响的过程，其他协变量作为可能需要控制的潜在混杂因素（和介质）。用于因果推断的绝大多数现有方法和系统假设所考虑的所有变量都是分类的或数值的（例如，性别、价格、注册）。在本文中，我们介绍了Causal NLP，这是一个用于通过观察数据推断因果关系的工具包，除了传统的数值和分类变量外，还包括文本。Causal NLP使用Meta学习器进行治疗效果估计，并支持使用原始文本及其语言属性作为治疗，结果或“受控”变量（例如，混杂物）。该库是开源的，可在https://github.com/amaiya/causalnlp上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
