🧬 nonlinear-causal#

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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.

The proposed model is:

_images/nl_causal.png
\[\text{(Stage 1)} \quad \phi(x) = \mathbf{z}^\prime \boldsymbol{\theta} + w, \qquad \text{(Stage 2)} \quad y = \beta \phi(x) + \mathbf{z}^\prime \boldsymbol{\alpha} + \epsilon\]

🎯 What We Can Do#

  • Estimate marginal causal effect \(\beta\)

  • Hypothesis testing (HT) and confidence interval (CI) for marginal causal effect \(\beta\).

  • Estimate nonlinear causal link \(\phi(\cdot)\).

#️⃣ 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}
}

📒 Contents#