nonlinear-causal is a Python module for nonlinear causal inference built on top of Two-stage methods.
Project description
🧬 nonlinear-causal
nonlinear-causal is a Python module for nonlinear causal inference, including hypothesis testing and confidence interval for causal effect, built on top of two-stage methods.
- GitHub repo: https://github.com/nl-causal/nonlinear-causal
- PyPi: https://pypi.org/project/nonlinear-causal/
- Open Source: MIT license
- Paper: arXiv:2209.08889
- Documentation: https://nonlinear-causal.readthedocs.io
The proposed model is:
$$ \phi(x) = \mathbf{z}^\prime \mathbf{\theta} + w, \quad y = \beta \phi(x) + \mathbf{z}^\prime \mathbf{\alpha} + \epsilon $$
- $\beta$: marginal causal effect from X -> Y;
- $\phi(\cdot)$: nonlinear causal link;
What We Can Do:
- Estimate $\theta$ and $\beta$.
- Hypothesis testing (HT) and confidence interval (CI) for marginal causal effect $\beta$.
- Estimate nonlinear causal link $\phi(\cdot)$.
Installation
Install nonlinear-causal
using pip
pip install nonlinear-causal
Install the latest version in Github:
pip install git+https://github.com/nl-causal/nonlinear-causal
Examples and notebooks
Reference
If you use this code please star 🌟 the repository and cite the following paper:
- Dai, B., Li, C., Xue, H., Pan, W., & Shen, X. (2022). Inference of nonlinear causal effects with GWAS summary data. arXiv preprint arXiv:2209.08889.
@article{dai2022inference,
title={Inference of nonlinear causal effects with GWAS summary data},
author={Dai, Ben and Li, Chunlin and Xue, Haoran and Pan, Wei and Shen, Xiaotong},
journal={arXiv preprint arXiv:2209.08889},
year={2022}
}
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