semvec/pypi-packages huggingface dataset:
DEEP-causal 0.0.22
DEEP_causal

# DEEP_causal

DEEP_causal is a Python library for discovering causal heterogeneity. The mechanism can be found in the publication "Causal heterogeneity discovery by bottom-up pattern search".

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install DEEP_causal. Install_requires=["gsq==0.1.6", "pandas==1.3.4", "numpy==1.21.4"]

```bash 
pip install gsq==0.1.6
pip install pandas==1.3.4
pip install numpy==1.21.4
pip install DEEP_causal
```

## Usage

```python
from DEEP_causal import compute_DEEP_causal
import pandas as pd

# read example training data and testing data
# example data is available at https://drive.google.com/drive/folders/1UW_bcZ7d_ygTXRjJlfR4JrvFKJwWq30D
train_data = pd.read_csv("example_train.csv")
test_data = pd.read_csv("example_test.csv")

# parents, treatment, outcome
parents_trtout = ["x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "w", "y"]   # from MMPC

# returns patterns and causal effect
train_output, test_output = compute_DEEP_causal.compute_dc(train_data, parents_trtout, test_data)
```
Dean-Kawasaki-Discretizations 0.1.0a6
Collection of solvers for Dean-Kawasaki
Requires Python: >=3.6

## Dean-Kawasaki Discretizations

This repository contains code that solves the Dean-Kawasaki equations in arbitrary number of 
dimensions using arbitrary many particles and produces arbitrary many sample paths. The 
brunt of the code is vectorized using numpy to allow for quickly computing these samples
on a large scale.