# Integrating Unstructured Text into Causal Inference: Empirical Evidence from Real Data

arXiv: https://arxiv.org/abs/2602.14274 · HTML: https://mlsanity.com/paper/2602.14274/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 15 Feb 2026.

## Builds on (most cited first, 6 of 6)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [CausalNLP: A Practical Toolkit for Causal Inference with Text](https://mlsanity.com/paper/2106.08043/) (2021)
- [Variable Importance Matching for Causal Inference](https://mlsanity.com/paper/2302.11715/) (2023)

## Built on by (most cited first, 2 of 2)

- [Causal Inference with Unstructured Treatments](https://mlsanity.com/paper/2608.00657/) (2026)
- [Causal Inference with Unstructured Outcomes](https://mlsanity.com/paper/2608.03085/) (2026)

## Abstract (arXiv)

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.