# A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks

arXiv: https://arxiv.org/abs/2311.11749 · HTML: https://mlsanity.com/paper/2311.11749/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 84 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Deep Structural Causal Models for Tractable Counterfactual Inference](https://mlsanity.com/paper/2006.06485/) (2020)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Deep Learning for Road Traffic Forecasting: Does it Make a Difference?](https://mlsanity.com/paper/2012.02260/) (2020)
- [Context-aware multi-head self-attentional neural network model for next location prediction](https://mlsanity.com/paper/2212.01953/) (2022)
- [Observational-Interventional Priors for Dose-Response Learning](https://mlsanity.com/paper/1605.01573/) (2016)
- [Understanding Predictability and Exploration in Human Mobility](https://mlsanity.com/paper/1608.01939/) (2016)
- [Evidence for a Conserved Quantity in Human Mobility](https://mlsanity.com/paper/1609.03526/) (2016)
- [Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis](https://mlsanity.com/paper/2210.10010/) (2022)
- [Controlling for Biasing Signals in Images for Prognostic Models: Survival Predictions for Lung Cancer with Deep Learning](https://mlsanity.com/paper/1904.00942/) (2019)
- [Characteristics of human mobility patterns revealed by high-frequency cell-phone position data](https://mlsanity.com/paper/1907.03604/) (2019)
- [Explaining the Power-law Distribution of Human Mobility Through Transportation Modality Decomposition](https://mlsanity.com/paper/1408.4910/) (2014)
- [20 Years of Mobility Modeling & Prediction: Trends, Shortcomings & Perspectives](https://mlsanity.com/paper/1906.07451/) (2019)

## Built on by (most cited first, 1 of 1)

- [Synthetic location trajectory generation using categorical diffusion models](https://mlsanity.com/paper/2402.12242/) (2024)

## Abstract (arXiv)

Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending how various aspects of mobility behavior affect predictions. This study introduces a causal intervention framework to assess the impact of mobility-related factors on neural networks designed for next location prediction -- a task focusing on predicting the immediate next location of an individual. To achieve this, we employ individual mobility models to synthesize location visit sequences and control behavior dynamics by intervening in their data generation process. We evaluate the interventional location sequences using mobility metrics and input them into well-trained networks to analyze performance variations. The results demonstrate the effectiveness in producing location sequences with distinct mobility behaviors, thereby facilitating the simulation of diverse yet realistic spatial and temporal changes. These changes result in performance fluctuations in next location prediction networks, revealing impacts of critical mobility behavior factors, including sequential patterns in location transitions, proclivity for exploring new locations, and preferences in location choices at population and individual levels. The gained insights hold value for the real-world application of mobility prediction networks, and the framework is expected to promote the use of causal inference to enhance the interpretability and robustness of neural networks in mobility applications.