This paper proposes that balancing functions in debiased machine learning should originate from the Neyman orthogonal score, rather than solely relying on covariates. The authors argue that while covariate balancing is suitable when regression error depends only on covariates, it can leave treatment-specific components unbalanced in cases of heterogeneous treatment effects. They advocate for regressor balancing using Riesz regression with basis functions of the full regressor as a more general principle for debiased machine learning. AI
影响 Introduces a new theoretical framework for debiased machine learning, potentially improving causal inference in complex datasets.
排序理由 This is a research paper published on arXiv discussing a theoretical advancement in debiased machine learning.
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