研究人员开发了一种新的局部学习方法来选择因果效应估计中的协变量，无需预处理或因果充分性假设。该方法确定了一个局部边界，用于高效搜索有效的调整集，提高了计算效率。在合成数据和真实世界数据上的实验表明，该方法能够准确估计因果效应，并显著加快了速度。 AI
影响 简化了复杂的因果推断任务，可能支持更强大的AI模型评估和开发。
排序理由 该集群包含一篇在arXiv上发表的学术论文，详细介绍了一种新的统计方法。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2502.07295v2 Announce Type: replace Abstract: Neural Networks (NNs) for causal effect estimation have shown strong empirical performance, yet endowing them with desirable semiparametric properties -- doubly robustness and fast convergence rates -- remains challenging. A com…
arXiv:2605.21548v1 Announce Type: new Abstract: We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal structure learning over all variables, or on strong assumptions such as causal suffi…
We study the problem of selecting covariates for unbiased estimation of the total causal effect.Existing approaches typically rely on global causal structure learning over all variables, or on strong assumptions such as causal sufficiency - where observed variables share no laten…