Metadata-Version: 2.1
Name: DistributionIV
Version: 0.0.0.dev0
Summary: Distributional Instrumental Variable Method
Author: Xinwei Shen
Author-email: xinwei.shen@stat.math.ethz.ch
License: BSD 3-Clause License
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: engression

# Distributional Instrumental Variable Method (DIV)

Distributional Instrumental Variable Method is a neural network-based method to estimate distributional causal effects (interventional distributions) proposed in the paper "[*Distributional Instrumental Variable Method*]()" by A. Holovchak, S. Saengkyongam, N. Meinshausen and X. Shen (2025). 


## Installation
The latest release of the Python package can be installed through pip:
```sh
pip install div
```


## Usage Example

```python
from div import DIV

## Fit a DIV model to data (x, y, z)
model = DIV(1, 1, 1, num_layer=4)
model.train(x, y, z, 10000, print_every_iter=1000)

## Evaluation
x_eval = torch.linspace(x.min(), x.max(), 5000).unsqueeze(1)
## interventional mean function
y_est_mean = model.predict_causal(x_eval, sample_size=1000)
```


## Contact information
If you meet any problems with the code, please submit an issue or contact [Xinwei Shen](mailto:xinwei.shen@stat.math.ethz.ch).
