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：封隔是一种精确估计实验材料不均匀时因果效应的技术,它涉及根据造成非异性的共同变量对现有的实验材料进行分层,然后在这些层(称为区块)中随机进行处理。这消除了共同变量对因果效应的意外影响。我们调查了如何找到一套稳定的共变量来形成区块的问题,以尽量减少因果效应估计的差异。我们利用基本因果图提供了一种有效的算法,以获得一套通用半马尔科维安因模型。