Metadata-Version: 2.4
Name: mawiisurv
Version: 0.1.0
Summary: Semiparametric Causal Inference for Right-Censored Outcomes with Many Weak Invalid Instruments
Author-email: Qiushi Bu <buqiushi17@mails.ucas.ac.cn>
License: MIT
Keywords: Censored outcomes,Deep neural networks,instrumental variable,generalized empirical likelihood,Mendelian randomization,Over-identification test,Semiparametric theory,Weak and invalid instruments
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.19
Requires-Dist: torch>=1.8
Requires-Dist: scipy>=1.5
Requires-Dist: scikit-learn>=0.24
Requires-Dist: xgboost>=1.3
Requires-Dist: numba>=0.53

# mawiisurv

`mawiisurv` implements G‐estimation methods for treatment effects under endogeneity, both with and without right‐censoring, using a variety of machine‐learning and classical estimators.

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## Features

- **Complete‐data G‐estimator** (`mawii_noncensor`)  
- **Right‐censoring G‐estimator** (`mawii_censor`)  
- Multiple model backends:
  - Neural networks
  - Linear regression
  - Random forests
  - XGBoost  
- Choice of Generalized Empirical Likelihood (GEL) functions:
  - Empirical Tilting (ET)
  - Empirical Likelihood (EL)
  - Continuous Updating Estimator (CUE)

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## Installation

Install from PyPI:

```bash
pip install mawiisurv



Dependencies
Make sure you have the following installed (the minimal compatible versions shown):
numpy>=1.19
torch>=1.8
scipy>=1.5
scikit-learn>=0.24
xgboost>=1.3
numba>=0.53
