ABC3通过主动学习提升随机实验效率，降低处理组与对照组不平衡。
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments
- 基于贝叶斯框架，以最小化后验方差为准则选择样本
- 实测在真实数据集上效率最高，且显著减少组间不平衡
- 适合需高效设计实验的研究者，尤其关注因果推断的可靠性
在因果推断中，随机实验是克服观察性研究理论缺陷的主流方法，但实验设计成本高昂，亟需高效方案。本文提出ABC3，一种用于因果推断的贝叶斯主动学习策略。我们证明：最小化条件平均处理效应估计误差等价于最小化整合后验方差，类似于Cohn准则。理论上，ABC3同时最小化处理组与对照组的不平衡及一类错误概率。其中，平衡性最小化特性尤为突出，因多项研究强调其重要性。在多个真实数据集上的广泛实验表明，ABC3实现了最高效率，且经验验证了理论结论成立。
原文摘要 · Abstract (English)
In causal inference, randomized experiment is a de facto method to overcome various theoretical issues in observational study. However, the experimental design requires expensive costs, so an efficient experimental design is necessary. We propose ABC3, a Bayesian active learning policy for causal inference. We show a policy minimizing an estimation error on conditional average treatment effect is equivalent to minimizing an integrated posterior variance, similar to Cohn criteria \citep{cohn1994active}. We theoretically prove ABC3 also minimizes an imbalance between the treatment and control groups and the type 1 error probability. Imbalance-minimizing characteristic is especially notable as several works have emphasized the importance of achieving balance. Through extensive experiments on real-world data sets, ABC3 achieves the highest efficiency, while empirically showing the theoretical results hold.
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