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API Reference

This section provides comprehensive documentation for all classes and functions in the dte_adj package. The API is organized into logical groups based on functionality and use cases.

Overview

The dte_adj package provides several types of estimators for computing distribution treatment effects:

  • Simple Randomization Estimators: For estimating distributional effects in simple randomized experiments where treatment assignment is independent of all covariates
  • Covariate Adaptive Randomization Estimators: For estimating distributional effects under covariate-adaptive randomization (CAR) designs, including stratified block randomization and other adaptive schemes
  • Local Distribution Estimators: For estimating local distribution treatment effects weighted by treatment propensity within strata
  • Utility Functions: Helper functions for confidence intervals and statistical computations
  • Plotting Utilities: Visualization tools for treatment effects and distributions

For theoretical foundations, see Byambadalai et al. (2024) [1] for simple randomization, Byambadalai et al. (2025) [2] for covariate-adaptive randomization, and Byambadalai et al. (2024) [4] for imperfect compliance scenarios.

For multi-task learning approaches that train models for all locations simultaneously (using is_multi_task=True), see the neural network framework in [3].

[1]Byambadalai, U., Oka, T., & Yasui, S. (2024). Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction. In Proceedings of the 41st International Conference on Machine Learning (ICML'24). arXiv:2407.16037.
[2]Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2025). On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization. In Proceedings of the 42nd International Conference on Machine Learning (ICML'25). arXiv:2506.05945.
[3]Hirata, T., Byambadalai, U., Oka, T., Yasui, S., & Uto, S. (2025). Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks. arXiv preprint arXiv:2507.07738.
[4]Byambadalai, U., Hirata, T., Oka, T., & Yasui, S. (2024). Beyond the Average: Distributional Causal Inference under Imperfect Compliance. arXiv preprint arXiv:2509.15594.

Detailed Documentation

.. toctree::
   :maxdepth: 2

   api/simple
   api/stratified
   api/local
   api/plot