论文标题
部分可观测时空混沌系统的无模型预测
A Systematic Paradigm for Detecting, Surfacing, and Characterizing Heterogeneous Treatment Effects (HTE)
论文作者
论文摘要
为了有效地优化和个性化治疗，有必要研究治疗效果的异质性。随着在许多在线受控实验中对广泛的用户进行处理，手动调查异质性每个维度的典型方法变得过于麻烦，容易出现主观人类偏见。我们需要一种有效的方法来搜索数千个目标协变量和数百个分解维度的实验。在本文中，我们提出了一个系统的范式，用于检测，表面和表征异质治疗效果。首先，我们在指定任何崩溃之前检测实验中是否存在治疗效果变化。其次，我们表现出异质性最相关的维度。最后，我们通过研究估计的个体治疗效果的条件分布来表征异质性，而不仅仅是条件平均治疗效应（CATE）。我们使用模拟数据和经验研究表明了我们方法的有效性。
To effectively optimize and personalize treatments, it is necessary to investigate the heterogeneity of treatment effects. With the wide range of users being treated over many online controlled experiments, the typical approach of manually investigating each dimension of heterogeneity becomes overly cumbersome and prone to subjective human biases. We need an efficient way to search through thousands of experiments with hundreds of target covariates and hundreds of breakdown dimensions. In this paper, we propose a systematic paradigm for detecting, surfacing and characterizing heterogeneous treatment effects. First, we detect if treatment effect variation is present in an experiment, prior to specifying any breakdowns. Second, we surface the most relevant dimensions for heterogeneity. Finally, we characterize the heterogeneity beyond just the conditional average treatment effects (CATE) by studying the conditional distributions of the estimated individual treatment effects. We show the effectiveness of our methods using simulated data and empirical studies.
