用迁移学习提升小样本下的因果效应估计精度
Transfer learning for causal forest
- 通过中间模型估计源域与目标域分布偏移，改进因果森林
- 理论证明目标域CATE误差受中间模型误差制约
- 在模拟和真实数据上均表现优异，适合小样本因果推断
迁移学习旨在将知识从一个领域迁移到另一个领域。传统方法通常将大量观测的源域模型适配到观测较少的目标域以提升性能。本文研究模型分布发生偏移的情况，将迁移学习应用于因果森林（HTERF），旨在估计条件平均处理效应（CATE）。所采用的方法是王（2016）提出的偏移量修正法，将其拓展至因果分析场景，利用中间模型估计源域与目标域之间的分布偏移。主要结果为：HTERF在目标域上的CATE误差可被中间模型的误差所界定。模拟实验及真实数据集上的测试表明，该方法在多种设置下均表现良好。
原文摘要 · Abstract (English)
Transfer learning addresses the challenge of transfering knowledge from one domain to another. Traditional transfer learning focuses on adapting models trained on a source domain (with a lot of observations) to improve performance on a target domain (with few observations). In this work we consider the case of a model shift and we focus on the transfer learning applied to a causal forest namely HTERF. This causal forest aims to estimate the Conditional Average Treatment Effect (CATE). The approach considered is the offset method presented by Wang (2016) adapted to a causal context. This method relies on the use of intermediate models in order to estimate the offset between source and target distributions. Our main result is a bound on the CATE error of HTERF on target depending on the error of the intermediate models. Simulation studies show the good performances of this approach in different settings on simulations and on a real-world dataset.
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