Recursive Partitioning for Heterogeneous Causal Effects
异质性因果效应的递归划分AI 解读 ↓
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异质性因果效应的递归划分
- 研究问题
- 如何用数据发现处理效应较大或较小的子群，并在实验或观察研究中对这些子群之间的效应差异进行有效推断？
- 方法与思路
- 作者将递归划分用于处理效应预测，针对个体因果效应无法直接观测的问题，提出专门的交叉验证准则。
- 材料陈述的贡献
- 摘要称方法支持数据驱动的效应异质性探索，模拟展示其优于标准方法的情形，并应用于搜索结果重排序实验。
- 阅读关注点
- 全文应核对观察研究的识别条件、划分与推断的关系，以及交叉验证准则如何处理不可观测效应和多重检验问题。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或被期刊接收；摘要未说明完整识别条件及推断有效性的适用边界。
原文摘要
In this paper we study the problems of estimating heterogeneity in causal effects in experimental or observational studies and conducting inference about the magnitude of the differences in treatment effects across subsets of the population. In applications, our method provides a data-driven approach to determine which subpopulations have large or small treatment effects and to test hypotheses about the differences in these effects. For experiments, our method allows researchers to identify heterogeneity in treatment effects that was not specified in a pre-analysis plan, without concern about invalidating inference due to multiple testing. In most of the literature on supervised machine learning (e.g. regression trees, random forests, LASSO, etc.), the goal is to build a model of the relationship between a unit's attributes and an observed outcome. A prominent role in these methods is played by cross-validation which compares predictions to actual outcomes in test samples, in order to select the level of complexity of the model that provides the best predictive power. Our method is closely related, but it differs in that it is tailored for predicting causal effects of a treatment rather than a unit's outcome. The challenge is that the "ground truth" for a causal effect is not observed for any individual unit: we observe the unit with the treatment, or without the treatment, but not both at the same time. Thus, it is not obvious how to use cross-validation to determine whether a causal effect has been accurately predicted. We propose several novel cross-validation criteria for this problem and demonstrate through simulations the conditions under which they perform better than standard methods for the problem of causal effects. We then apply the method to a large-scale field experiment re-ranking results on a search engine.