mlsanityData to 7 Oct 2026

CausalNLP: A Practical Toolkit for Causal Inference with Text

arXiv 2106.08043 ↗First posted 15 Jun 2021cs.CL cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

15 of 29 references.

References · 15

  1. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  2. 2016Wide & Deep Learning for Recommender Systemscited by 745
  3. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  4. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  5. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 299
  6. 2020CausalML: Python Package for Causal Machine Learningcited by 74
  7. 2019Machine Learning Methods Economists Should Know Aboutcited by 63
  8. 2018How to Make Causal Inferences Using Textscited by 59
  9. 2020Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimatescited by 59
  10. 2020Causal Effects of Linguistic Propertiescited by 39
  11. 2018Challenges of Using Text Classifiers for Causal Inferencecited by 33
  12. 2020ktrain: A Low-Code Library for Augmented Machine Learningcited by 23
3 more
  1. 2018Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Qualitycited by 21
  2. 2019Estimating Causal Effects of Tone in Online Debatescited by 19
  3. 2021Operationalizing Complex Causes: A Pragmatic View of Mediationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 1.

5 papers.

  1. 2024A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospectscited by 6
  2. 2023Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimationcited by 3
  3. 2025LLM-Driven Treatment Effect Estimation Under Inference Time Text Confoundingcited by 2
  4. 2024Eliciting Causal Abilities in Large Language Models for Reasoning Taskscited by 0
  5. 2026Integrating Unstructured Text into Causal Inference: Empirical Evidence from Real Datacited by 0