Randomized experiments are often performed to study the causal effects of interest. Blocking is a technique to precisely estimate the causal effects when the experimental material is not homogeneous. It involves stratifying the available experimental material based on the covariates causing non-homogeneity and then randomizing the treatment within those strata (known as blocks). This eliminates the unwanted effect of the covariates on the causal effects of interest. We investigate the problem of finding a stable set of covariates to be used to form blocks, that minimizes the variance of the causal effect estimates. Using the underlying causal graph, we provide an efficient algorithm to obtain such a set for a general semi-Markovian causal model.

翻译：随机实验通常用于研究感兴趣的因果效应。当实验材料并非同质时，分组是一种能够精确估计因果效应的技术。该方法基于导致异质性的协变量对可用实验材料进行分层，然后在各层（即分组）内随机化处理，从而消除协变量对目标因果效应产生的不必要影响。我们研究了如何寻找一组稳定的协变量用于形成分组，以最小化因果效应估计的方差。基于底层因果图，我们提出了一种高效算法，用于在一般半马尔可夫因果模型中获取这样的协变量集。