# Directed Graphical Models and Causal Discovery for Zero-Inflated Data

@article{Yu2020DirectedGM, title={Directed Graphical Models and Causal Discovery for Zero-Inflated Data}, author={Shiqing Yu and Mathias Drton and Ali Shojaie}, journal={arXiv: Methodology}, year={2020} }

Modern RNA sequencing technologies provide gene expression measurements from single cells that promise refined insights on regulatory relationships among genes. Directed graphical models are well-suited to explore such (cause-effect) relationships. However, statistical analyses of single cell data are complicated by the fact that the data often show zero-inflated expression patterns. To address this challenge, we propose directed graphical models that are based on Hurdle conditional…

## One Citation

### Sequential Learning of the Topological Ordering for the Linear Non-Gaussian Acyclic Model with Parametric Noise

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