The Challenges of Hyperparameter Tuning for Accurate Causal Effect Estimation
准确估计因果效应时的超参数调优挑战AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要以观测数据中的平均和个体化处理效应估计为核心，调参与模型比较均服务于因果推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
准确估计因果效应时的超参数调优挑战
- 研究问题
- 观测数据中的因果效应估计应如何调参，估计器、基础学习器、超参数和代理评价指标分别对最终表现有多大影响？
- 方法与思路
- 作者在四个因果推断基准数据集上组合常用因果估计器、基础学习器与调参指标，开展大规模经验比较。
- 材料陈述的贡献
- 摘要报告调参提高了达到其最先进表现标准的概率，同时指出常用指标跨场景表现不一致，尚缺统一可靠的评价指标。
- 阅读关注点
- 应核对基准效应真值来源、调参预算和数据划分，辨明“最先进表现”的判据，并检查平均效应与个体化效应的评价差异。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要中的改善限于所考察组合，不能推出某个指标或估计器普遍最优。
原文摘要
ML is playing an increasingly crucial role in estimating causal effects of treatments on outcomes from observational data. Many ML methods (`causal estimators') have been proposed for this task. All of these methods, as with any ML approach, require extensive hyperparameter tuning. For non-causal predictive tasks, there is a consensus on the choice of tuning metrics (e.g. mean squared error), making it simple to compare models. However, for causal inference tasks, such a consensus is yet to be reached, making any comparison of causal models difficult. On top of that, there is no ideal metric on which to tune causal estimators, so one must rely on proxies. Furthermore, the fact that model selection in causal inference involves multiple components (causal estimator, ML regressor, hyperparameters, metric), complicates the issue even further. In order to evaluate the importance of each component, we perform an extensive empirical study on their combination. Our experimental setup involves many commonly used causal estimators, regressors (`base learners' henceforth) and metrics applied to four well-known causal inference benchmark datasets. Our results show that hyperparameter tuning increased the probability of reaching state-of-the-art performance in average ($65\% {\rightarrow} 81\%$) and individualised ($50\% {\rightarrow} 57\%$) effect estimation with only commonly used estimators. We also show that the performance of standard metrics can be inconsistent across different scenarios. Our findings highlight the need for further research to establish whether metrics uniformly capable of state-of-the-art performance in causal model evaluation can be found.