Nonlinear-causal

I’m thrilled to announce the launch of our newest Python library, "nonlinear-causal v1.0" nonlinear-causal is a Python module for nonlinear causal inference, including hypothesis testing and confidence interval for causal effect, built on top of instrument variables and Two-Stage least squares.

`nonlinear-causal` primary implements **Two-Stage Least Squares (2SLS)** and **Two-Stage Inverse Regression (2SIR)** based on Instrumental Variable (IV) regression.

PS: 2SIR is the method proposed in our paper, which was just accepted in [PMLRCLeaR](https://arxiv.org/abs/2209.08889).

You can access the library via [github](https://github.com/statmlben/nonlinear-causal) and find its documentation on [doc](https://nonlinear-causal.readthedocs.io/en/latest/index.html). We sincerely look forward to your valuable expertise and insights.

Cheer!

- fix circular import issues
- readthedoc v1 -> v2

o.1
nonlinear-causal - Version 0.1 - Initial Release

We are excited to announce the release of our new software on **causal inference**, *nonlinear-causal* Version 1.0

**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 (2SLS and 2SIR).

We hope you enjoy using *nl_causal*! This release is an important step in our journey. As we continue to innovate and improve, we look forward to delivering an enhanced version. Thank you for your support!