随机实验中的最优方差缩减
Optimal Variance Reduction in Randomized Experiments
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中文总结 AI 辅助
本文提出一种利用辅助信息的随机实验方差缩减方法，可适配任意复杂度模型，对可加处理指标最优减方差，对复杂指标用Delta Method，还探讨了建模考量。
中文摘要 AI 辅助
本文介绍一种利用辅助信息（不受处理影响的响应协变量）实现随机实验方差缩减的方法。与Double ML[Chernozhukov 2018]类似，可采用任意复杂度的模型，无需担心过拟合或正则化导致的偏差。对于可加处理指标，该方法可最优地最小化方差；对于更复杂的处理指标（如乘法和比率），则最小化方差的Delta Method估计值。本文还通过多个示例探讨了建模注意事项。
英文摘要
This paper describes an approach to variance reduction in randomized experiments using side information (covariates of the response unaffected by treatment). As with Double ML[Chernozhukov 2018], models of arbitrary complexity may be employed without concern for bias due to overfitting or regularization. For additive treatment metrics, the approach minimizes variance optimally. For more complex treatment metrics (e.g., multiplicative and ratios) the Delta Method estimate of variance is minimized. Through a variety of examples, the paper also explores modeling considerations.
