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[2602.14274] Integrating Unstructured Text into Causal Inference: Empirical Evidence from Real Data
Summary
This paper presents a framework for integrating unstructured text into causal inference, demonstrating its effectiveness against traditional structured data methods.
Why It Matters
The ability to utilize unstructured text for causal inference expands analytical capabilities in scenarios where structured data is limited. This is particularly relevant for businesses that rely on data-driven decisions but may lack comprehensive datasets.
Key Takeaways
- Unstructured text can effectively inform causal inference, traditionally reliant on structured data.
- The proposed framework leverages transformer-based language models for analysis.
- Empirical evidence shows consistent results between estimates from unstructured text and structured data.
- This approach enhances decision-making capabilities in data-scarce environments.
- The findings validate the integration of NLP techniques in causal inference tasks.
Computer Science > Machine Learning arXiv:2602.14274 (cs) [Submitted on 15 Feb 2026] Title:Integrating Unstructured Text into Causal Inference: Empirical Evidence from Real Data Authors:Boning Zhou, Ziyu Wang, Han Hong, Haoqi Hu View a PDF of the paper titled Integrating Unstructured Text into Causal Inference: Empirical Evidence from Real Data, by Boning Zhou and 3 other authors View PDF HTML (experimental) Abstract:Causal inference, a critical tool for informing business decisions, traditionally relies heavily on structured data. However, in many real-world scenarios, such data can be incomplete or unavailable. This paper presents a framework that leverages transformer-based language models to perform causal inference using unstructured text. We demonstrate the effectiveness of our framework by comparing causal estimates derived from unstructured text against those obtained from structured data across population, group, and individual levels. Our findings show consistent results between the two approaches, validating the potential of unstructured text in causal inference tasks. Our approach extends the applicability of causal inference methods to scenarios where only textual data is available, enabling data-driven business decision-making when structured tabular data is scarce. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.14274 [cs.LG] (or arXiv:2602.14274v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv....
