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<p>The R-learner is a widely used method for estimating heterogeneous treatment effects (HTE) and offers considerable flexibility in causal inference applications. However, its effectiveness decreases when treatment assignment is endogenous, as it relies exclusively on observed covariates. To address this limitation, the RLASSO-IV method is proposed, which extends the R-learner by incorporating instrumental variables (IV) to mitigate endogeneity. The least absolute shrinkage and selection operator (LASSO) regularization approach is employed to facilitate variable selection and enhance the accuracy of treatment effect estimation. Extensive simulation studies indicate that RLASSO-IV achieves lower estimation error than existing machine learning-based methods for causal inference. Our proposed method is further evaluated on real-world application examining the effect of education on wages, the results show that it captures treatment effect heterogeneity more accurately and robustly than benchmark approaches. These results highlight the potential of RLASSO-IV as a reliable approach for individualized causal effect estimation in the presence of endogeneity.</p>

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Heterogeneous treatment effects estimation using R-learner with instrumental variable

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Rafiullah,
  • Hong Wang

摘要

The R-learner is a widely used method for estimating heterogeneous treatment effects (HTE) and offers considerable flexibility in causal inference applications. However, its effectiveness decreases when treatment assignment is endogenous, as it relies exclusively on observed covariates. To address this limitation, the RLASSO-IV method is proposed, which extends the R-learner by incorporating instrumental variables (IV) to mitigate endogeneity. The least absolute shrinkage and selection operator (LASSO) regularization approach is employed to facilitate variable selection and enhance the accuracy of treatment effect estimation. Extensive simulation studies indicate that RLASSO-IV achieves lower estimation error than existing machine learning-based methods for causal inference. Our proposed method is further evaluated on real-world application examining the effect of education on wages, the results show that it captures treatment effect heterogeneity more accurately and robustly than benchmark approaches. These results highlight the potential of RLASSO-IV as a reliable approach for individualized causal effect estimation in the presence of endogeneity.