# Fast & Efficient Learning of Bayesian Networks from Data: Knowledge Discovery and Causality

arXiv: https://arxiv.org/abs/2310.09222 · HTML: https://mlsanity.com/paper/2310.09222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 7 of 7)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks](https://mlsanity.com/paper/2010.05788/) (2020)
- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [Theory Refinement on Bayesian Networks](https://mlsanity.com/paper/1303.5709/) (2013)
- [Causality-based Feature Selection: Methods and Evaluations](https://mlsanity.com/paper/1911.07147/) (2019)
- [LaPLACE: Probabilistic Local Model-Agnostic Causal Explanations](https://mlsanity.com/paper/2310.00570/) (2023)

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

- [Enhanced Interpretable Knowledge Tracing for Students Performance Prediction with Human understandable Feature Space](https://mlsanity.com/paper/2509.18231/) (2025)

## Abstract (arXiv)

Structure learning is essential for Bayesian networks (BNs) as it uncovers causal relationships, and enables knowledge discovery, predictions, inferences, and decision-making under uncertainty. Two novel algorithms, FSBN and SSBN, based on the PC algorithm, employ local search strategy and conditional independence tests to learn the causal network structure from data. They incorporate d-separation to infer additional topology information, prioritize conditioning sets, and terminate the search immediately and efficiently. FSBN achieves up to 52% computation cost reduction, while SSBN surpasses it with a remarkable 72% reduction for a 200-node network. SSBN demonstrates further efficiency gains due to its intelligent strategy. Experimental studies show that both algorithms match the induction quality of the PC algorithm while significantly reducing computation costs. This enables them to offer interpretability and adaptability while reducing the computational burden, making them valuable for various applications in big data analytics.