Interventionism analyses causal influence in terms of correlations of changes under a distribution of interventions. But the correspondence between correlated changes and causal influence is not obvious. I probe its plausibility with a problem-case involving variables related as time derivative (velocity) to integral (position), such that the latter variable must change given an intervention on the former unless dependencies are introduced among the testing and controlling interventions. Under the orthodox criteria such interventions will fail to be appropriate for causal analysis. I consider various alternatives, including permitting control interventions to be chancy, restricting the available models and mitigating variation of off-path variables. None of these work. I then present a fourth suggestion which modifies the interventionist criteria in order to permit interventions which can influence other variables than just their own targets. The correspondence between correlated changes and causal influence can thereby saved when dependencies are introduced among such interventions. This modification and the required dependencies, I argue, are perfectly in line with practice and may also assist in a wider class of cases.

干预主义根据干预分布下变化的相关性分析因果影响。但相关变化与因果影响之间的对应关系并不明显。我用一个涉及与时间导数（速度）到积分（位置）相关的变量的问题案例来探讨它的合理性，这样后一个变量必须在对前者进行干预的情况下发生变化，除非在测试和控制干预之间引入依赖关系。在正统的标准下，此类干预措施将不适用于因果分析。我考虑了各种替代方案，包括允许控制干预是偶然的，限制可用模型和减轻偏离路径变量的变化。这些都不起作用。然后我提出第四个建议，它修改了干预主义标准，以允许干预可以影响其他变量，而不仅仅是他们自己的目标。因此，当在这些干预措施之间引入依赖关系时，可以保存相关变化和因果影响之间的对应关系。我认为，这种修改和所需的依赖关系完全符合实践，也可能有助于更广泛的案例。