VanderWeele Tyler J, Richardson Thomas S
Harvard School of Public Health, Department of Epidemiology, 677 Huntington Avenue, Boston, MA 02115,
University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195,
Ann Stat. 2012;40(4):2128-2161. doi: 10.1214/12-aos1019.
The sufficient-component cause framework assumes the existence of sets of sufficient causes that bring about an event. For a binary outcome and an arbitrary number of binary causes any set of potential outcomes can be replicated by positing a set of sufficient causes; typically this representation is not unique. A sufficient cause interaction is said to be present if within all representations there exists a sufficient cause in which two or more particular causes are all present. A singular interaction is said to be present if for some subset of individuals there is a unique minimal sufficient cause. Empirical and counterfactual conditions are given for sufficient cause interactions and singular interactions between an arbitrary number of causes. Conditions are given for cases in which none, some or all of a given set of causes affect the outcome monotonically. The relations between these results, interactions in linear statistical models and Pearl's probability of causation are discussed.
充分病因组件框架假定存在导致某一事件的充分病因集。对于二元结局和任意数量的二元病因,任何一组潜在结局都可以通过设定一组充分病因来复制;通常这种表示并不唯一。如果在所有表示中都存在一个充分病因,其中两个或更多特定病因都存在,则称存在充分病因相互作用。如果对于某些个体子集存在唯一的最小充分病因,则称存在单一相互作用。给出了任意数量病因之间充分病因相互作用和单一相互作用的经验条件和反事实条件。给出了给定病因集中无、部分或全部病因对结局产生单调影响的情况的条件。讨论了这些结果、线性统计模型中的相互作用与Pearl因果概率之间的关系。