Metadata-Version: 2.1
Name: DEEP_causal
Version: 0.0.22
Summary: DEEP_causal
Home-page: https://pypi.org/project/DEEP-causal/
Author: S.Zhang
Author-email: shisheng.zhang@mymail.unisa.edu.au
License: UNKNOWN
Description: # 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)
        ```
Platform: UNKNOWN
Description-Content-Type: text/markdown
