aiqbench analysis
This research proposes an offset method adapted from Wang (2016) to enable transfer learning within causal forests for estimating Conditional Average Treatment Effects (CATE) under model shift. By deriving a theoretical bound on CATE error dependent on intermediate model performance, the study validates the approach through simulations and real-world data, offering a rigorous framework for adapting causal inference models across domains with limited target observations.
Transfer learning for causal forest
arXiv:2606.07693v1 Announce Type: cross Abstract: 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.
Abstract: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.
Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)
arXiv:2606.07693 [stat.ML]
(or arXiv:2606.07693v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2606.07693 Focus to learn more arXiv-issued DOI via DataCite
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