基于因果的图形化测试, 以获得随机化实验的最佳屏蔽设置 (A Causality-based Graphical Test to obtain an Optimal Blocking Set for Randomized Experiments)

from arxiv, 14 pages, 9 figures, Accepted for presentation at Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice (CSDNeurIPS) workshop. 35th Conference on Neural Information Processing Systems (NeurIPS 2021),Sydney, Australia

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. We formalize the problem of obtaining a statistically optimal set of covariates to be used to create blocks while performing a randomized experiment. We provide a graphical test to obtain such a set for a general semi-Markovian causal model. We also propose and provide ideas towards solving a more general problem of obtaining an optimal blocking set that considers both the statistical and economic costs of blocking.

翻译：限制是一种精确估计实验材料不均匀时的因果关系的技术。我们正式确定了在进行随机试验时获得一套统计上最佳的共变物的问题。我们提供了一种图形测试,以获得一套用于一般半马尔科维因模式的通用半马尔科维因模式。我们还提出和提出一些想法,以解决获得一套考虑到封堵的统计成本和经济成本的最佳阻塞物的最普遍问题。