causalexplain 0.5.2

A package to extract the causal graph from continuous tabular data.

causalexplain - A library to infer causal-effect relationships from tabular data

'causalexplain' is a library that implements methods to extract the causal graph, from tabular data, specifically the ReX method, and other compared methods like GES, PC, FCI, LiNGAM, CAM, and NOTEARS.

ReX is a causal discovery method that leverages machine learning (ML) models coupled with explainability techniques, specifically Shapley values, to identify and interpret significant causal relationships among variables. Comparative evaluations on synthetic datasets comprising tabular data reveal that ReX outperforms state-of-the-art causal discovery methods across diverse data generation processes, including non-linear and additive noise models. Moreover, ReX was tested on the Sachs single-cell protein-signaling dataset, achieving a precision of 0.952 and recovering key causal relationships with no incorrect edges. Taking together, these results showcase ReX’s effectiveness in accurately recovering true causal structures while minimizing false positive pre- dictions, its robustness across diverse datasets, and its applicability to real-world problems. By combining ML and explainability techniques with causal discovery, ReX bridges the gap between predictive modeling and causal inference, offering an effective tool for understanding complex causal structures.

It is built using SKLearn estimators, so that it can be used in scikit-learn pipelines and (hyper)parameter search, while facilitating testing (including some API compliance), documentation, open source development, packaging, and continuous integration.

The datasets used in the examples can be generated using the generators module, which is also part of this library. But in case you want to reproduce results from the articles that we used as reference, you can find the datasets in the data folder.

Prerequisites without Docker

• Operating System: Linux or macOS
• Environment Manager: PyEnv or Conda
• Programming Language: Python 3.10.12 or higher
• Hardware: CPU

Installation

The project can be installed using pip:

$ pip install causalexplain

Data

The datasets used to reproduce the results presented in the manuscript are available under the data folder. The datasets were generated using the generators module.

Executing causalexplain

To run causalexplain on your data, you can use the causalexplain command:

$ python -m causalexplain
   ___                      _                 _       _       
  / __\__ _ _   _ ___  __ _| | _____  ___ __ | | __ _(_)_ __  
 / /  / _` | | | / __|/ _` | |/ _ \ \/ / '_ \| |/ _` | | '_ \ 
/ /__| (_| | |_| \__ \ (_| | |  __/>  <| |_) | | (_| | | | | |
\____/\__,_|\__,_|___/\__,_|_|\___/_/\_\ .__/|_|\__,_|_|_| |_|
                                       |_|                                        
usage: causalexplain [-h] -d DATASET [-m {rex,pc,fci,ges,lingam,cam,notears}] 
                   [-t TRUE_DAG] [-l LOAD_MODEL] [-T THRESHOLD] [-u UNION] 
                   [-i ITERATIONS] [-b BOOTSTRAP] [-r REGRESSOR] [-S SEED] 
                   [-s [SAVE_MODEL]] [-n] [-v] [-q] [-o OUTPUT]

that will present you with a menu to choose the dataset you want to use, the method you want to use to infer the causal graph, and the hyperparameters you want to use.

The minimum required to run causalexplain is a dataset file in CSV format, with the first row containing the names of the variables, and the rest of the rows containing the values of the variables. The method selected by default is ReX, but you can also choose between PC, FCI, GES, LiNGAM, CAM, NOTEARS. At the end of the execution, the edges of the plausible causal graph will be displayed along with the metrics obtained, if the true dag is provided (argument -t).

Example commands

The following command illustrates how to run causalexplain on the toy dataset using the ReX method:

$ python -m causalexplain -d /path/to/toy_dataset.csv -t /path/to/toy_dataset.dot

The same command can be used to run causalexplain on the toy dataset using the CAM method:

$ python -m causalexplain -d /path/to/toy_dataset.csv -m cam -t /path/to/toy_dataset.dot

For more information on command line options, run causalexplain -h or go to the Quickstart section in the documentation.

Additional Information

WIP