论文标题
在存在陷阱变量的情况下，用小数据的因果效应估计
Estimation of causal effects with small data in the presence of trapdoor variables
论文作者
论文摘要
当不适用众所周知的后门和前门调整时，我们考虑了从观察数据中估算干预措施因果关系影响的问题。我们表明，当可识别的因果效应受到有条件独立关系无法推论的隐式功能约束时，因果效应的估计量可能在小样本中表现出偏见。该偏差与我们称为陷阱门变量的变量有关。我们使用模拟数据来研究不同的策略，以说明陷阱门变量，并提出如何将相关的诱捕器偏差最小化。通过生命课程1971-2002研究中的实际数据，说明了陷阱门变量在因果效应估计中的重要性。使用此数据集，我们估计教育对芬兰背景下收入的因果影响。贝叶斯建模使我们能够考虑参数不确定性，并将估计的因果效应作为后验分布。
We consider the problem of estimating causal effects of interventions from observational data when well-known back-door and front-door adjustments are not applicable. We show that when an identifiable causal effect is subject to an implicit functional constraint that is not deducible from conditional independence relations, the estimator of the causal effect can exhibit bias in small samples. This bias is related to variables that we call trapdoor variables. We use simulated data to study different strategies to account for trapdoor variables and suggest how the related trapdoor bias might be minimized. The importance of trapdoor variables in causal effect estimation is illustrated with real data from the Life Course 1971-2002 study. Using this dataset, we estimate the causal effect of education on income in the Finnish context. Bayesian modelling allows us to take the parameter uncertainty into account and to present the estimated causal effects as posterior distributions.
