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Consider the problem of improving the estimation of conditional averagetreatment effects (CATE) for a target domain of interest by leveraging relatedinformation from a source domain with a different feature space. Thisheterogeneous transfer learning problem for CATE estimation is ubiquitous inareas such as healthcare where we may wish to evaluate the effectiveness of atreatment for a new patient population for which different clinical covariatesand limited data are available. In this paper, we address this problem byintroducing several building blocks that use representation learning to handlethe heterogeneous feature spaces and a flexible multi-task architecture withshared and private layers to transfer information between potential outcomefunctions across domains. Then, we show how these building blocks can be usedto recover transfer learning equivalents of the standard CATE learners. On anew semi-synthetic data simulation benchmark for heterogeneous transferlearning we not only demonstrate performance improvements of our heterogeneoustransfer causal effect learners across datasets, but also provide insights intothe differences between these learners from a transfer perspective.

Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation