A novel problem of improving causal effect estimation accuracy with the help of knowledge transfer under the same covariate (or feature) space setting, i.e., homogeneous transfer learning (TL), is studied, referred to as the Transfer Causal Learning (TCL) problem. While most recent efforts in adapting TL techniques to estimate average causal effect (ACE) have been focused on the heterogeneous covariate space setting, those methods are inadequate for tackling the TCL problem since their algorithm designs are based on the decomposition into shared and domain-specific covariate spaces. To address this issue, we propose a generic framework called \texttt{$\ell_1$-TCL}, which incorporates $\ell_1$ regularized TL for nuisance parameter estimation and downstream plug-in ACE estimators, including outcome regression, inverse probability weighted, and doubly robust estimators. Most importantly, with the help of Lasso for high-dimensional regression, we establish non-asymptotic recovery guarantees for the generalized linear model (GLM) under the sparsity assumption for the proposed \texttt{$\ell_1$-TCL}. Moreover, the success of \texttt{$\ell_1$-TCL} could inspire the adaptations of many recently proposed principled approaches in statistics literature to be adapted to this novel TCL problem. From an empirical perspective, \texttt{$\ell_1$-TCL} is a generic learning framework that can incorporate not only GLM but also many recently developed non-parametric methods, which can enhance robustness to model mis-specification. We demonstrate this empirical benefit through extensive experiments using GLM and recent neural network based \texttt{$\ell_1$-TCL} on both benchmark semi-synthetic and real datasets, which shows improved performance compared with existing TL approaches for ACE estimation.

翻译：研究了一种在相同协变量（或特征）空间设置下通过知识迁移提升因果效应估计精度的新问题，即同构迁移学习问题，并将其称为迁移因果学习问题。尽管近期在将迁移学习技术应用于平均因果效应估计方面的努力主要集中在异构协变量空间设置上，但这些方法因算法设计依赖于共享与领域特定协变量空间的分解，不足以解决这一问题。为此，我们提出一个通用框架\texttt{$\ell_1$-TCL}，该框架融合了$\ell_1$正则化迁移学习用于 nuisance 参数估计，并集成了下游的即插即用型平均因果效应估计器，包括结果回归、逆概率加权和双重稳健估计器。尤为重要的是，在高维回归中借助Lasso方法，我们为所提出的\texttt{$\ell_1$-TCL}在稀疏性假设下的广义线性模型建立了非渐近恢复保证。此外，\texttt{$\ell_1$-TCL}的成功可启发统计文献中许多近期提出的原则性方法进行适配，以应用于这一新颖的迁移因果学习问题。从实证角度看，\texttt{$\ell_1$-TCL}是一个通用学习框架，不仅可融入广义线性模型，还可融入多种近期发展的非参数方法，从而增强对模型误设的鲁棒性。我们通过使用广义线性模型和基于神经网络的\texttt{$\ell_1$-TCL}在基准半合成数据集和真实数据集上开展的大量实验，展示了这一实证优势，结果表明与现有用于平均因果效应估计的迁移学习方法相比，其性能更优。