# Causal Perception in Question-Answering Systems

arXiv: https://arxiv.org/abs/2012.14477 · HTML: https://mlsanity.com/paper/2012.14477/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [A Diachronic Perspective on User Trust in AI under Uncertainty](https://mlsanity.com/paper/2310.13544/) (2023)
- [XInsight: eXplainable Data Analysis Through The Lens of Causality](https://mlsanity.com/paper/2207.12718/) (2022)
- [Question answering systems for health professionals at the point of care -- a systematic review](https://mlsanity.com/paper/2402.01700/) (2024)
- [Exploring Effectiveness of Explanations for Appropriate Trust: Lessons from Cognitive Psychology](https://mlsanity.com/paper/2210.03737/) (2022)

## Abstract (arXiv)

Root cause analysis is a common data analysis task. While question-answering systems enable people to easily articulate a why question (e.g., why students in Massachusetts have high ACT Math scores on average) and obtain an answer, these systems often produce questionable causal claims. To investigate how such claims might mislead users, we conducted two crowdsourced experiments to study the impact of showing different information on user perceptions of a question-answering system. We found that in a system that occasionally provided unreasonable responses, showing a scatterplot increased the plausibility of unreasonable causal claims. Also, simply warning participants that correlation is not causation seemed to lead participants to accept reasonable causal claims more cautiously. We observed a strong tendency among participants to associate correlation with causation. Yet, the warning appeared to reduce the tendency. Grounded in the findings, we propose ways to reduce the illusion of causality when using question-answering systems.