# Causal Effects of Linguistic Properties

arXiv: https://arxiv.org/abs/2010.12919 · HTML: https://mlsanity.com/paper/2010.12919/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 30 of 39)

- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Inducing Causal Structure for Interpretable Neural Networks](https://mlsanity.com/paper/2112.00826/) (2021)
- [Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction](https://mlsanity.com/paper/2105.06965/) (2021)
- [Causal Proxy Models for Concept-Based Model Explanations](https://mlsanity.com/paper/2209.14279/) (2022)
- [Causal Distillation for Language Models](https://mlsanity.com/paper/2112.02505/) (2021)
- [End-To-End Causal Effect Estimation from Unstructured Natural Language Data](https://mlsanity.com/paper/2407.07018/) (2024)
- [Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatments](https://mlsanity.com/paper/2410.00903/) (2024)
- [Causal Estimation for Text Data with (Apparent) Overlap Violations](https://mlsanity.com/paper/2210.00079/) (2022)
- [Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies](https://mlsanity.com/paper/2203.09672/) (2022)
- [Towards Compositional Interpretability for XAI](https://mlsanity.com/paper/2406.17583/) (2024)
- [FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training](https://mlsanity.com/paper/2210.03167/) (2022)
- [Proximal Causal Inference With Text Data](https://mlsanity.com/paper/2401.06687/) (2024)
- [Contrastive representations of high-dimensional, structured treatments](https://mlsanity.com/paper/2411.19245/) (2024)
- [Text-Transport: Toward Learning Causal Effects of Natural Language](https://mlsanity.com/paper/2310.20697/) (2023)
- [Preference Learning for AI Alignment: a Causal Perspective](https://mlsanity.com/paper/2506.05967/) (2025)
- [Counterfactual Maximum Likelihood Estimation for Training Deep Networks](https://mlsanity.com/paper/2106.03831/) (2021)
- [CausalNLP: A Practical Toolkit for Causal Inference with Text](https://mlsanity.com/paper/2106.08043/) (2021)
- [Causal Inference from Text: Unveiling Interactions between Variables](https://mlsanity.com/paper/2311.05286/) (2023)
- [Estimating Causal Effects of Text Interventions Leveraging LLMs](https://mlsanity.com/paper/2410.21474/) (2024)
- [Interventional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2104.11681/) (2021)
- [Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects](https://mlsanity.com/paper/2109.07542/) (2021)
- [Quantifying the Task-Specific Information in Text-Based Classifications](https://mlsanity.com/paper/2110.08931/) (2021)
- [A Design-based Solution for Causal Inference with Text: Can a Language Model Be Too Large?](https://mlsanity.com/paper/2510.08758/) (2025)
- [Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts](https://mlsanity.com/paper/2202.00848/) (2022)
- [DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation](https://mlsanity.com/paper/2406.00611/) (2024)
- [Isolated Causal Effects of Natural Language](https://mlsanity.com/paper/2410.14812/) (2024)
- [LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding](https://mlsanity.com/paper/2507.02843/) (2025)
- [Leveraging Generative Artificial Intelligence for Causal Inference with Unstructured Data](https://mlsanity.com/paper/2507.03897/) (2025)
- [Causal Representation Learning for Generalisable Recommendation](https://mlsanity.com/paper/2605.27043/) (2026)
- [Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community](https://mlsanity.com/paper/2110.07938/) (2021)

## Abstract (arXiv)

We consider the problem of using observational data to estimate the causal effects of linguistic properties. For example, does writing a complaint politely lead to a faster response time? How much will a positive product review increase sales? This paper addresses two technical challenges related to the problem before developing a practical method. First, we formalize the causal quantity of interest as the effect of a writer's intent, and establish the assumptions necessary to identify this from observational data. Second, in practice, we only have access to noisy proxies for the linguistic properties of interest -- e.g., predictions from classifiers and lexicons. We propose an estimator for this setting and prove that its bias is bounded when we perform an adjustment for the text. Based on these results, we introduce TextCause, an algorithm for estimating causal effects of linguistic properties. The method leverages (1) distant supervision to improve the quality of noisy proxies, and (2) a pre-trained language model (BERT) to adjust for the text. We show that the proposed method outperforms related approaches when estimating the effect of Amazon review sentiment on semi-simulated sales figures. Finally, we present an applied case study investigating the effects of complaint politeness on bureaucratic response times.