Suppose we want to estimate a total effect with covariate adjustment in a linear structural equation model. We have a causal graph to decide what covariates to adjust for, but are uncertain about the graph. Here, we propose a testing procedure, that exploits the fact that there are multiple valid adjustment sets for the target total effect in the causal graph, to perform a robustness check on the graph. If the test rejects, it is a strong indication that we should not rely on the graph. We discuss what mistakes in the graph our testing procedure can detect and which ones it cannot and develop two strategies on how to select a list of valid adjustment sets for the procedure. We also connect our result to the related econometrics literature on coefficient stability tests.

翻译：假设我们想要在线性结构方程模型中估算一个总效果,同时进行共变调整。 我们有一个因果图表可以决定要调整的共变数, 但是对图形却不确定。 在这里, 我们提出一个测试程序, 利用因果图中目标总效果有多重有效调整集这一事实, 对图表进行稳健性检查。 如果测试拒绝, 它强烈地表明我们不应该依赖图表。 我们讨论我们的测试程序在图形中可以检测哪些错误, 哪些错误不能检测, 并就如何选择一个有效的调整组列表制定两个战略 。 我们还将我们的结果与相关的系数稳定性测试的计量经济学文献联系起来 。