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.

翻译：在因果推断中，随机化实验是克服观察性研究中各种理论问题的实际标准方法。然而，实验设计需要高昂的成本，因此高效的实验设计是必要的。我们提出了ABC3，一种用于因果推断的贝叶斯主动学习策略。我们证明了最小化条件平均处理效应估计误差的策略等价于最小化积分后验方差，这与Cohn准则相似。我们从理论上证明了ABC3还能最小化处理组与对照组之间的不平衡性和第一类错误概率。最小化不平衡的特性尤为显著，因为已有若干研究强调了实现平衡的重要性。通过在真实世界数据集上的大量实验，ABC3实现了最高的效率，同时从经验上验证了理论结果成立。