# Quantifying the Causal Effects of Conversational Tendencies

arXiv: https://arxiv.org/abs/2009.03897 · HTML: https://mlsanity.com/paper/2009.03897/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Causal Estimation for Text Data with (Apparent) Overlap Violations](https://mlsanity.com/paper/2210.00079/) (2022)
- [AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments](https://mlsanity.com/paper/2410.08917/) (2024)
- [A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms](https://mlsanity.com/paper/2209.06367/) (2022)
- [A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing](https://mlsanity.com/paper/2210.16604/) (2022)
- [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)
- [LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding](https://mlsanity.com/paper/2507.02843/) (2025)
- [IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model](https://mlsanity.com/paper/2209.03891/) (2022)
- [A Similarity Measure for Comparing Conversational Dynamics](https://mlsanity.com/paper/2507.18956/) (2025)
- [Time is On My Side: Dynamics of Talk-Time Sharing in Video-chat Conversations](https://mlsanity.com/paper/2506.20474/) (2025)
- [Supervised Learning Objectives for High-Dimensional Treatment Effects](https://mlsanity.com/paper/2605.27281/) (2026)
- [Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors](https://mlsanity.com/paper/2609.04350/) (2026)

## Abstract (arXiv)

Understanding what leads to effective conversations can aid the design of better computer-mediated communication platforms. In particular, prior observational work has sought to identify behaviors of individuals that correlate to their conversational efficiency. However, translating such correlations to causal interpretations is a necessary step in using them in a prescriptive fashion to guide better designs and policies. In this work, we formally describe the problem of drawing causal links between conversational behaviors and outcomes. We focus on the task of determining a particular type of policy for a text-based crisis counseling platform: how best to allocate counselors based on their behavioral tendencies exhibited in their past conversations. We apply arguments derived from causal inference to underline key challenges that arise in conversational settings where randomized trials are hard to implement. Finally, we show how to circumvent these inference challenges in our particular domain, and illustrate the potential benefits of an allocation policy informed by the resulting prescriptive information.