stochpw 0.1.0

Stochastic gradient descent implementation of permutation weighting

stochpw - Permutation Weighting for Causal Inference

Permutation weighting learns importance weights for causal inference by training a discriminator to distinguish between observed treatment-covariate pairs and artificially permuted pairs.

Installation

pip install stochpw  # Coming soon

For development:

git clone https://github.com/yourusername/stochpw.git
cd stochpw
poetry install

Quick Start

import jax.numpy as jnp
from stochpw import PermutationWeighter

# Your observational data
X = jnp.array(...)  # Covariates, shape (n_samples, n_features)
A = jnp.array(...)  # Treatments, shape (n_samples, 1)

# Fit permutation weighter (sklearn-style API)
weighter = PermutationWeighter(
    num_epochs=100,
    batch_size=256,
    random_state=42
)
weighter.fit(X, A)

# Predict importance weights
weights = weighter.predict(X, A)

# Use weights for causal inference (in external package)
# ate = weighted_estimator(Y, A, weights)

How It Works

Permutation weighting estimates density ratios by:

1. Training a discriminator to distinguish:

   • Permuted pairs: (X, A') with label C=1 (treatments shuffled)
   • Observed pairs: (X, A) with label C=0 (original data)

2. Extracting weights from discriminator probabilities:

   w(a, x) = η(a, x) / (1 - η(a, x))
   

   where η(a, x) = p(C=1 | a, x)

3. Using weights for inverse probability weighting in causal effect estimation

Composable Design

The package exposes low-level components for integration into larger models:

from stochpw import (
    create_training_batch,
    logistic_loss,
    extract_weights,
    create_linear_discriminator
)

# Use in your custom architecture (e.g., DragonNet)
batch = create_training_batch(X, A, batch_indices, rng_key)
logits = my_discriminator(params, batch.A, batch.X)
loss = logistic_loss(logits, batch.C)

Features

• JAX-based: Fast, GPU-compatible, auto-differentiable
• Sklearn-style API: Familiar .fit() and .predict() interface
• Composable: All components exposed for integration
• Flexible: Supports binary, continuous, and multi-dimensional treatments
• Diagnostic tools: ESS, SMD, and balance checks included

References

Arbour, D., Dimmery, D., & Sondhi, A. (2021). Permutation Weighting. International Conference on Machine Learning (ICML).

License

MIT License - see LICENSE file for details.

Contributing

See CONTRIBUTING.md for guidelines.

Citation

If you use this package, please cite:

@software{stochpw2024,
  title = {stochpw: Permutation Weighting for Causal Inference},
  author = {Your Name},
  year = {2024},
  url = {https://github.com/yourusername/stochpw}
}