# Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test

arXiv: https://arxiv.org/abs/2211.16596 · HTML: https://mlsanity.com/paper/2211.16596/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Causal discovery in heavy-tailed models](https://mlsanity.com/paper/1908.05097/) (2019)
- [Structure learning for extremal tree models](https://mlsanity.com/paper/2012.06179/) (2020)
- [Inducing Social Optimality in Games via Adaptive Incentive Design](https://mlsanity.com/paper/2204.05507/) (2022)
- [Causal Analysis at Extreme Quantiles with Application to London Traffic Flow Data](https://mlsanity.com/paper/2108.10215/) (2021)
- [Distributed Dynamic Pricing of Multiscale Transportation Networks](https://mlsanity.com/paper/1902.00946/) (2019)
- [Dynamic Tolling for Inducing Socially Optimal Traffic Loads](https://mlsanity.com/paper/2110.08879/) (2021)

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

- [ReBeCA: Unveiling Interpretable Behavior Hierarchy behind the Iterative Self-Reflection of Language Models with Causal Analysis](https://mlsanity.com/paper/2602.06373/) (2026)

## Abstract (arXiv)

Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory. However, current methods for causal discovery are often unable to uncover causal links, between random variables in a dynamic setting, that manifest only when the variables first experience low-probability realizations. To address this issue, we introduce a novel statistical independence test on data collected from time-invariant dynamical systems in which rare but consequential events occur. In particular, we exploit the time-invariance of the underlying data to construct a superimposed dataset of the system state before rare events happen at different timesteps. We then design a conditional independence test on the reorganized data. We provide non-asymptotic sample complexity bounds for the consistency of our method, and validate its performance across various simulated and real-world datasets, including incident data collected from the Caltrans Performance Measurement System (PeMS). Code containing the datasets and experiments is publicly available.