# A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks

arXiv: https://arxiv.org/abs/2007.03248 · HTML: https://mlsanity.com/paper/2007.03248/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

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

- [Causal Discovery from Temporal Data: An Overview and New Perspectives](https://mlsanity.com/paper/2303.10112/) (2023)
- [Open problems in causal structure learning: A case study of COVID-19 in the UK](https://mlsanity.com/paper/2305.03859/) (2023)
- [Identifying Unique Spatial-Temporal Bayesian Network without Markov Equivalence](https://mlsanity.com/paper/2211.10085/) (2022)
- [Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks](https://mlsanity.com/paper/2308.10606/) (2023)

## Abstract (arXiv)

Dynamic Bayesian networks have been well explored in the literature as discrete-time models: however, their continuous-time extensions have seen comparatively little attention. In this paper, we propose the first constraint-based algorithm for learning the structure of continuous-time Bayesian networks. We discuss the different statistical tests and the underlying hypotheses used by our proposal to establish conditional independence. Furthermore, we analyze and discuss the computational complexity of the best and worst cases for the proposed algorithm. Finally, we validate its performance using synthetic data, and we discuss its strengths and limitations comparing it with the score-based structure learning algorithm from Nodelman et al. (2003). We find the latter to be more accurate in learning networks with binary variables, while our constraint-based approach is more accurate with variables assuming more than two values. Numerical experiments confirm that score-based and constraint-based algorithms are comparable in terms of computation time.