causalexplain 0.9.1
A package to extract the causal graph from continuous tabular data.
License: MIT License
Requires Python: >=3.10
Keywords: causal inference, causal graph, data science

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# 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.

![ReX Schema](https://raw.githubusercontent.com/renero/causalexplain/main/docs/_static/REX.png)

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 3.13.11 or higher
- Hardware: CPU (CUDA/MPS optional)

## Installation

The project can be installed using pip:

```bash
$ pip install causalexplain
```

## What's new in v0.9.1

- GUI refactor: split the NiceGUI app into tab modules with shared helpers.
- GUI styling: extracted CSS into static assets and smoothed Train tab resizing.
- GUI tests: added coverage for helper utilities and Cytoscape layouts.
- Packaging: include GUI CSS assets in package data.

## 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 --help
   ___                      _                 _       _
  / __\__ _ _   _ ___  __ _| | _____  ___ __ | | __ _(_)_ __
 / /  / _` | | | / __|/ _` | |/ _ \ \/ / '_ \| |/ _` | | '_ \
/ /__| (_| | |_| \__ \ (_| | |  __/>  <| |_) | | (_| | | | | |
\____/\__,_|\__,_|___/\__,_|_|\___/_/\_\ .__/|_|\__,_|_|_| |_|
                                       |_|
usage: causalexplain [-h] [-a | --shap-sampling | --no-shap-sampling]
                     [-b BOOTSTRAP] [-B BOOTSTRAP_PARALLEL_JOBS]
                     [-c {union,intersection}] [-C]
                     [-d DATASET] [--gui] [-H MAX_SHAP_SAMPLES]
                     [-i ITERATIONS] [-l LOAD_MODEL]
                     [-m {rex,pc,fci,ges,lingam,cam,notears}]
                     [-M] [-n] [-o OUTPUT] [-P PARALLEL_JOBS] [-p PRIOR]
                     [-q] [-s [SAVE_MODEL]] [-S SEED] [-T THRESHOLD]
                     [-t TRUE_DAG] [-v]```
```

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`).

#### GUI mode

To use the local GUI, run:

```bash
$ python -m causalexplain --gui
```

This launches a browser-based app for training models, loading/evaluating saved
runs, and generating synthetic datasets, all on your local machine (port 8080).

### 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:

Note: If the notebook kernel cannot import `causalexplain`, run the notebook
from the repo root, or install the package (`pip install -e .`), or add the
repo root to `sys.path` (e.g.: `sys.path.insert(0, str(pathlib.Path("..").resolve()))
`).
For higher-quality math text in plots, install a LaTeX distribution; otherwise
pass `usetex=False` when plotting.
See `examples/simple_experiment.ipynb` for a working notebook example.

```python
from causalexplain import GraphDiscovery

experiment = GraphDiscovery(
   experiment_name='toy_experiment',
   model_type='rex',
   csv_filename='../data/toy_dataset.csv',
   true_dag_filename='../data/toy_dataset.dot')

# Run the experiments
experiment.run(hpo_iterations=10, bootstrap_iterations=10, combine_op='union', quiet=True)

# Plot the resulting DAG (avoid LaTeX/Graphviz dependencies when running locally)
experiment.plot(show_metrics=True, layout='circular', usetex=False)
````

To load a model from a file, you can use the `load` method of the
`GraphDiscovery` class:

```python
from causalexplain import GraphDiscovery

experiment = GraphDiscovery()
experiment.load("/path/to/model.pkl")
```

## Adaptive SHAP sampling

For direct SHAP usage in notebooks, the explainability module exposes a
high-level wrapper that defaults to adaptive sampling:

```python
from causalexplain.explainability.shapley import compute_shap

# default: adaptive_shap_sampling=True
res, diag = compute_shap(X, model, backend="kernel", adaptive_shap_sampling=True)

# disable (may be slow for large m)
res, diag = compute_shap(X, model, backend="kernel", adaptive_shap_sampling=False)
```

CLI example (same executable shown above):

```bash
python -m causalexplain --shap-sampling
python -m causalexplain --no-shap-sampling
```

Available SHAP backends are `kernel`, `gradient`, `explainer`, and `tree`.
ReX defaults to `tree` when running the GBT regressor.

When adaptive sampling is enabled, the key knobs are `max_shap_samples`,
`K_max`, `max_explain_samples`, and `stratify`. These
control background size, repeated sampling for stability, and how many rows
are explained.

Note on large datasets: if `adaptive_shap_sampling=False` and `m > 2000`, the
tool warns about potential non-termination (the threshold is conservative).

### Why adaptive sampling is mathematically reasonable

Many SHAP explainers approximate an expectation over a background
distribution; using $n$ background points gives a Monte Carlo estimate. The
standard error scales approximately as $SE \sim \frac{1}{\sqrt{n}}$. So,
when sampling without replacement from a finite dataset of size $m$, the
finite population correction factor applies: $\frac{1}{\sqrt{1 - (n/m)}}$.

This means increasing $n$ yields diminishing returns, so capping the
background around 250 is a pragmatic speed/accuracy tradeoff. Repeating
the sampling ($K$ runs) provides a stability diagnostic: compute a global
importance vector per run as $\overline{\mid \text{SHAP} \mid}$ per feature,
then check variability (CV) and rank stability (Spearman correlation) across runs.

Backend-aware note: Kernel SHAP is particularly sensitive and expensive, so
caps like `max_explain_samples` matter most there. Gradient and generic
explainers often have different performance profiles, but still benefit from
controlled baselines/background sizes.

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.

```python
# Plot the resulting DAG
experiment.plot(show_metrics=True, layout='circular', usetex=False)

# 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:

```python
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:

```bash
$ 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:

```bash
$ 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](https://renero.github.io/causalexplain/quickstart.html)
section in the documentation.

You can also launch the GUI locally:

```bash
$ python -m causalexplain --gui
```

### Prior knowledge (ReX)

ReX can optionally use prior knowledge to constrain edge directions when you
already know a rough ordering of variables (for example, temporal tiers).
The prior is a JSON file with a single `prior` key whose value is a list of
tiers; each tier is a list of column names. Variables in earlier tiers may
cause variables in later tiers, but not vice versa. All names must match the
dataset columns, but you can omit variables you have no prior knowledge about.

Example JSON file:

```json
{
  "prior": [
    ["A", "B"],
    ["C", "D"]
  ]
}
```

Use it from the CLI with `-p`/`--prior` (ReX only):

```bash
$ python -m causalexplain -d /path/to/data.csv -p /path/to/prior.json
```

Or from a notebook:

```python
prior = [["A", "B"], ["C", "D"]]
experiment.run(prior=prior, hpo_iterations=10, bootstrap_iterations=10)
```

## Citation

If you use **CausalExplain**, please cite the **software** and/or the **related publication** below.

### Software
> Renero, J. (2026). *CausalExplain* (Version 0.9.1).
> Available at: [https://github.com/renero/causalexplain](https://github.com/renero/causalexplain)

**BibTeX**
```bibtex
@software{causalexplain_software,
  author  = {Jesús Renero},
  title   = {CausalExplain},
  version = {0.9.1},
  url     = {https://github.com/renero/causalexplain},
  date    = {2026-01-04}
}
```

### Related Publication

> Renero, J., Maestre, R., & Ochoa, I. (2026). ReX: Causal discovery based on machine learning and explainability techniques.
> *Pattern Recognition, 172*, 112491. [https://doi.org/10.1016/j.patcog.2025.112491](https://doi.org/10.1016/j.patcog.2025.112491)

**BibTeX**
```bibtex
@article{Renero2026ReX,
  author  = {Jesús Renero and Roberto Maestre and Idoia Ochoa},
  title   = {ReX: Causal discovery based on machine learning and explainability techniques},
  journal = {Pattern Recognition},
  volume  = {172},
  pages   = {112491},
  year    = {2026},
  doi     = {10.1016/j.patcog.2025.112491},
  url     = {https://doi.org/10.1016/j.patcog.2025.112491}
}
````