# Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios

arXiv: https://arxiv.org/abs/2610.16411 · HTML: https://mlsanity.com/paper/2610.16411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 11 of 11)

- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [Causal Influence Detection for Improving Efficiency in Reinforcement Learning](https://mlsanity.com/paper/2106.03443/) (2021)
- [Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies](https://mlsanity.com/paper/2103.16772/) (2021)
- [Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios](https://mlsanity.com/paper/2302.10135/) (2023)
- [Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions](https://mlsanity.com/paper/2210.16535/) (2022)
- [CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments](https://mlsanity.com/paper/2304.06848/) (2023)
- [A Causal-based Approach to Explain, Predict and Prevent Failures in Robotic Tasks](https://mlsanity.com/paper/2209.05255/) (2022)
- [ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications](https://mlsanity.com/paper/2402.16068/) (2024)
- [Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios](https://mlsanity.com/paper/2406.04955/) (2024)
- [Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments](https://mlsanity.com/paper/2504.11901/) (2025)
- [HEXAR: a Hierarchical Explainability Architecture for Robots](https://mlsanity.com/paper/2601.03070/) (2026)

## Abstract (arXiv)

Deploying robots in human-shared environments requires deciding not only how to navigate, but also when dedicated social signals are worth emitting. Classic observation-based inference can be misleading in such settings: confounding factors may bias the estimated relationship between a signal and the task outcome, leading to incorrect decisions. Causal inference provides a framework for disentangling these relationships by modelling the mechanisms linking observations, actions, and outcomes, and by estimating interventional effects after accounting for confounding paths. We study a human-robot interaction scenario in a corridor and use a causal model to estimate whether a social navigation signal causally improves transit success. We represent the interaction as a causal DAG involving robot action, pedestrian occupancy, available space, task outcome, and a controlled confounder (static obstacles). We compare the interventional estimate with the observational estimate obtained by conditioning only on the robot action, which does not explicitly model interventions. Our results show that accounting for confounding variables can reverse the conclusions drawn from naive observational analysis: the observational estimate wrongly suggests that the social signal is detrimental, whereas the causal estimate shows that it is beneficial. This discrepancy highlights how failing to account for confounding can lead to incorrect conclusions and demonstrates the importance of causal models for context-aware signalling decisions in social robot navigation.