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| # Summary | ||
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| `dte_adj` is a Python package designed for estimating distributional treatment effects (DTEs) in randomized experiments. Unlike traditional approaches that focus on average treatment effects, `dte_adj` enables researchers to analyze the full distributional impact of interventions across different outcome levels. The package implements machine learning-enhanced regression adjustment methods to achieve variance reduction, making distributional effect estimation more precise and computationally efficient. It supports multiple experimental designs including simple randomization, covariate-adaptive randomization (CAR), and local distributional treatment effect (LDTE) estimation. The package provides a scikit-learn compatible API and comprehensive functionality for computing distribution functions, probability treatment effects, and quantile treatment effects with confidence intervals. |
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読者層的にはA/Bテストっていう言い方しかわからない人もいるかも？と思いました。
randomized experiments (RCTs, also known as A/B tests) とかはどうでしょうか？
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| Randomized experiments have been fundamental to scientific inquiry since the pioneering work of @Fisher:1935, providing the gold standard for causal inference. While most experimental analyses focus on average treatment effects (ATEs), many research questions require understanding how treatments affect the entire distribution of outcomes, not just the mean. Distributional treatment effects (DTEs) capture these richer patterns, revealing heterogeneous impacts across different outcome levels that averages can mask. | ||
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| Despite the growing importance of distributional analysis in fields ranging from economics to medicine, the Python ecosystem lacks comprehensive tools for DTE estimation. While SciPy provides basic empirical cumulative distribution functions, it offers no specialized functionality for treatment effect estimation, variance reduction, or confidence interval construction in experimental settings. Existing R packages like `RDDtools` focus on regression discontinuity rather than randomized experiments, and lack modern machine learning integration. |
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比較するライブラリとしてはDoWhyやEconMLを追加すると良いかと思いました。EconMLは機械学習取り込んでますが、distributionalな話はしてないという言い方ができるかと思います。
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