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@@ -11,7 +11,7 @@ The dte_adj package provides several types of estimators for computing distribut
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* **Simple Randomization Estimators**: For estimating distributional effects in simple randomized experiments where treatment assignment is independent of all covariates
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* **Covariate Adaptive Randomization Estimators**: For estimating distributional effects under covariate-adaptive randomization (CAR) designs, including stratified block randomization and other adaptive schemes
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* **Local Distribution Estimators**: For estimating local distribution treatment effects weighted by treatment propensity within strata
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* **Utility Functions**: Helper functions for confidence intervals and statistical computations
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* **Utility Functions**: Helper functions for confidence intervals and statistical computations
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* **Plotting Utilities**: Visualization tools for treatment effects and distributions
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For theoretical foundations, see Byambadalai et al. (2024) [#simple2024]_ for simple randomization and Byambadalai et al. (2025) [#car2025]_ for covariate-adaptive randomization.
Regarding how to contribute to this package, please refer to https://github.com/CyberAgentAILab/python-dte-adjustment/blob/main/CONTRIBUTING.md for more details.
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Regarding how to contribute to this package, please refer to https://github.com/CyberAgentAILab/python-dte-adjustment/blob/main/CONTRIBUTING.md for more details.
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