CausalExplain Documentation#
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.
This repository contains the implementation of ReX and all necessary tools to reproduce the results presented in our accompanying paper. ReX supports diverse data generation processes, including non-linear and additive noise models, and has demonstrated robust performance on synthetic and real-world datasets.
About ReX#
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 predictions, 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.
Our experimental results, conducted on five families of synthetic datasets with varying complexity, demonstrate that REX consistently recovers true causal relationships with high precision while minimizing false positives and orientation errors, comparing favorably to existing methods. Additionally, REX was tested on the Sachs single-cell protein-signaling dataset (Sachs et al., 2005), achieving a competitive performance with no false positives and recovering important causal relationships. This further validates the applicability of REX to real-world datasets, highlighting its robustness across different types of data.
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 in the paper and the examples can be generated using the
generators module, which is also part of this library. In case you want to
reproduce results from the articles that we used as reference, you can find
the datasets in the data folder.
Executing causalexplain#
Option 1: Command Line#
To run causalexplain on your data, you can use the causalexplain command:
$ python -m causalexplain
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/ / / _` | | | / __|/ _` | |/ _ \ \/ / '_ \| |/ _` | | '_ \
/ /__| (_| | |_| \__ \ (_| | | __/> <| |_) | | (_| | | | | |
\____/\__,_|\__,_|___/\__,_|_|\___/_/\_\ .__/|_|\__,_|_|_| |_|
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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).
Option 2: Notebook#
In case you want to run causalexplain from your code in a notebook, you can
use the GraphDiscovery class. The following example shows how to use
the GraphDiscovery class to train a model on a dataset using ReX method:
from causalexplain import GraphDiscovery
experiment = GraphDiscovery(
experiment_name='my_experiment',
model_type='rex',
csv_filename='data.csv',
true_dag_filename='true_graph.dot')
# Run the experiments
experiment.run()
To load a model from a file, you can use the load method of the
GraphDiscovery class:
from causalexplain import GraphDiscovery
experiment = GraphDiscovery()
experiment.load("/path/to/model.pkl")
This can be useful if you want to train a model on a dataset and then use it to predict causal graphs on other datasets, or train a model on different batches.
Once a model has been trained or loaded, you can plot the resulting DAG, save the trained model to a file, or export the predicted causal graph to a DOT file.
# Plot the resulting DAG
experiment.plot()
# Save the trained model to a file
experiment.save("/path/to/model.pkl")
To export the predicted causal graph to a DOT file, you can use the export
method of the GraphDiscovery class:
experiment.export("/path/to/my_predicted_graph.dot")
Output#
The output of causalexplain is typically a graph with the edges of the
plausible causal graph and the metrics obtained from the evaluation of the
causal graph against the true DAG. These results are printed to the console,
unless the ‘-o’ option is specified, in which case the DAG is saved to a
file in DOT format. Metrics are printed only if the true DAG is provided.
Example CLI 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.