Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust
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随机对照试验协变量平衡方法的对抗鲁棒性局限
- 研究问题
- 满足常用协变量平衡标准的处理分配，是否仍可能产生很大的平均处理效应估计误差，以及如何寻找这些最坏情形？
- 方法与思路
- 作者开发优化算法 ATASTREET，在平衡约束允许的分配中寻找对抗性处理分配，并构造衡量试验接近最坏情形程度的指标。
- 材料陈述的贡献
- 摘要称，经验分析显示标准化均值差平衡标准和 Pocock 序贯分配法可能容许高误差分配，并提供寻找此类分配的算法。
- 阅读关注点
- 应核对对抗者可用的信息、分配可行域、误差目标及算法保证，并区分最坏情形存在与随机化下实际发生的概率。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给误差规模和发生概率，不能据此认定随机试验通常不可靠。
原文摘要
The first step towards investigating the effectiveness of a treatment via a randomized trial is to split the population into control and treatment groups then compare the average response of the treatment group receiving the treatment to the control group receiving the placebo. In order to ensure that the difference between the two groups is caused only by the treatment, it is crucial that the control and the treatment groups have similar statistics. Indeed, the validity and reliability of a trial are determined by the similarity of two groups' statistics. Covariate balancing methods increase the similarity between the distributions of the two groups' covariates. However, often in practice, there are not enough samples to accurately estimate the groups' covariate distributions. In this paper, we empirically show that covariate balancing with the Standardized Means Difference (SMD) covariate balancing measure, as well as Pocock's sequential treatment assignment method, are susceptible to worst-case treatment assignments. Worst-case treatment assignments are those admitted by the covariate balance measure, but result in highest possible ATE estimation errors. We developed an adversarial attack to find adversarial treatment assignment for any given trial. Then, we provide an index to measure how close the given trial is to the worst-case. To this end, we provide an optimization-based algorithm, namely Adversarial Treatment ASsignment in TREatment Effect Trials (ATASTREET), to find the adversarial treatment assignments.