Module causallib.contrib

This module currently includes additional causal methods contributed to the package by causal inference researchers other than causallib‘s core developers.

The causal models in this module can be slightly more novel then in the ones in estimation module. However, they should largely adhere to causallib API (e.g., IndividualOutcomeEstimator or WeightEstimator). Since code here is more experimental, models might also require additional (and less trivial) package dependencies, or have less test coverage.
Well-integrated models could be transferred into the main estimation module in the future.

Contributed Methods

Currently contributed methods are:

1. Adversarial Balancing: implementing the algorithm described in Adversarial Balancing for Causal Inference. .. code-block:: python

   from causallib.contrib.adversarial_balancing import AdversarialBalancing

2. Interpretable Subgroup Discovery in Treatment Effect Estimation: implementing the heterogeneous effect mixture model (HEMM) presented in Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines .. code-block:: python

   from causallib.contrib.hemm import HEMM

3. Matching Estimation/Transform using faiss.

   Implemented a nearest neighbors search with API that matches sklearn.NearestNeighbors but is powered by faiss for GPU support and much faster search on CPU as well.

   from causallib.contrib.faissknn import FaissNearestNeighbors
   

Dependencies

Each model might have slightly different requirements.
Refer to the documentation of each model for the additional packages it requires.

Requirements for contrib models are concentrated in contrib/requirements.txt and can be automatically installed using the extra-requirements contrib flag:
shell script pip install causallib[contrib] -f https://download.pytorch.org/whl/torch_stable.html
The -f find-links option is required to install PyTorch dependency.

References

Ozery-Flato, M., Thodoroff, P., Ninio, M., Rosen-Zvi, M., & El-Hay, T. (2018). Adversarial balancing for causal inference. arXiv preprint arXiv:1810.07406.

Nagpal, C., Wei, D., Vinzamuri, B., Shekhar, M., Berger, S. E., Das, S., & Varshney, K. R. (2020, April). Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines. In Proceedings of the ACM Conference on Health, Inference, and Learning (pp. 19-29).

Subpackages

• causallib.contrib.adversarial_balancing package
  • Submodules
    • causallib.contrib.adversarial_balancing.adversarial_balancing module
    • causallib.contrib.adversarial_balancing.classifier_selection module
  • Module contents
• Module causallib.contrib.hemm
  • Submodules
    • causallib.contrib.hemm.gen_synthetic_data module
    • causallib.contrib.hemm.hemm module
    • causallib.contrib.hemm.hemm_api module
    • causallib.contrib.hemm.hemm_metrics module
    • causallib.contrib.hemm.hemm_outcome_models module
    • causallib.contrib.hemm.hemm_utilities module
    • causallib.contrib.hemm.load_ihdp_data module
  • Module contents
• causallib.contrib.shared_sparsity_selection package
  • Submodules
    • causallib.contrib.shared_sparsity_selection.shared_sparsity_selection module
  • Module contents

Submodules

• causallib.contrib.faissknn module

Module contents