# References and citation

## Citation

If you use TreeIG in your work, please cite the
[TreeIG paper](https://www.ludgerhentschel.com/PDFs/Hentschel%20'26g.pdf):

```bibtex
@misc{hentschel2026treeig,
  author = {Hentschel, Ludger},
  title  = {{TreeIG}: Exact Integrated Gradients for Tree-Based Models},
  year   = {2026},
  url    = {https://www.ludgerhentschel.com/PDFs/Hentschel%20'26g.pdf},
}
```

## References

TreeIG:

- Hentschel, Ludger. 2026.
  ["TreeIG: Exact Integrated Gradients for Tree-Based Models."](https://www.ludgerhentschel.com/PDFs/Hentschel%20'26g.pdf)
  *https://www.ludgerhentschel.com/Research.html* and *https://www.ludgerhentschel.com/Programs.html*

Integrated Gradients:

- Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. 2017.
  ["Axiomatic Attribution for Deep Networks."](https://proceedings.mlr.press/v70/sundararajan17a.html)
  *Proceedings of the 34th International Conference on Machine Learning*,
  PMLR 70:3319–3328.

SHAP and TreeSHAP:

- Lundberg, Scott M., and Su-In Lee. 2017.
  "A Unified Approach to Interpreting Model Predictions."
  *Advances in Neural Information Processing Systems (NeurIPS)*.

- Lundberg, Scott M., Gabriel Erion, and Su-In Lee. 2020.
  "From Local Explanations to Global Understanding with Explainable AI for Trees."
  *Nature Machine Intelligence*.

Popular implementations of Integrated Gradients for smooth models:

- Captum for PyTorch: https://captum.ai/
- TensorFlow Integrated Gradients: https://www.tensorflow.org/tutorials/interpretability/integrated_gradients

## License

TreeIG is released under the terms in [LICENSE](https://github.com/LudgerHentschel/treeig/blob/main/LICENSE).

## Integrated Gradients BibTeX

```bibtex
@inproceedings{sundararajan2017axiomatic,
  title = {Axiomatic Attribution for Deep Networks},
  author = {Sundararajan, Mukund and Taly, Ankur and Yan, Qiqi},
  booktitle = {Proceedings of the 34th International Conference on Machine Learning},
  series = {Proceedings of Machine Learning Research},
  volume = {70},
  pages = {3319--3328},
  year = {2017},
  publisher = {PMLR},
  url = {https://proceedings.mlr.press/v70/sundararajan17a.html},
}
```