Front-door Adjustment Example

This example demonstrates using CausalFM for front-door adjustment.

Overview

In this example, we will:

1. Generate front-door training data with mediators

2. Train a front-door model

3. Evaluate on test data

4. Understand when front-door identification works

Coming Soon

This tutorial is under development. In the meantime, check out:

• Standard CATE Estimation Example - Complete Standard CATE example

• Data Generation - Front-door data generation guide

• Models - Front-door model usage

Quick Example

from causalfm.data import FrontdoorDataGenerator
from causalfm.models import FrontdoorModel
from causalfm.training import FrontdoorTrainer, TrainingConfig

# Generate front-door data
generator = FrontdoorDataGenerator(
    num_samples=1024,
    num_features=10,
    num_confounders=5
)
generator.generate_multiple(500, "data/frontdoor_train/")

# Train
if __name__ == '__main__':
    config = TrainingConfig(
        data_path="data/frontdoor_train/*.csv",
        epochs=100,
        save_dir="checkpoints/frontdoor/"
    )
    trainer = FrontdoorTrainer(config)
    trainer.train()

# Evaluate
model = FrontdoorModel.from_pretrained("checkpoints/frontdoor/best_model.pth")

# Use mediator m along with x, a, y
result = model.estimate_cate(
    x_train, m_train, a_train, y_train, x_test
)
cate = result['cate']

For a complete working example, see the notebook at: evaluation/notebook/test_fd.ipynb