For the purpose of causal inference we employ a stochastic model of the data generating process, utilizing individual propensity probabilities for the treatment, and also individual and counterfactual prognosis probabilities for the outcome. We assume a generalized version of the stable unit treatment value assumption, but we do not assume any version of strongly ignorable treatment assignment. Instead of conducting a sensitivity analysis, we utilize the principle of maximum entropy to estimate the distribution of causal effects. We develop a principled middle-way between extreme explanations of the observed data: we do not conclude that an observed association is wholly spurious, and we do not conclude that it is wholly causal. Rather, our conclusions are tempered and we conclude that the association is part spurious and part causal. In an example application we apply our methodology to analyze an observed association between marijuana use and hard drug use.

翻译：为实现因果推断，我们采用数据生成过程的随机模型，利用个体治疗倾向概率以及个体与反事实预后概率作为结果变量。我们假设了稳定单元处理值假设的广义版本，但未采用任何形式的强可忽略处理分配假设。我们运用最大熵原理而非敏感性分析来估计因果效应的分布。我们在观测数据的极端解释之间建立了原则性的中间路径：既不认定观测关联完全虚假，也不认定其完全因果。相反，我们的结论是审慎的——判定该关联部分虚假、部分因果。在示例应用中，我们运用该方法分析了吸食大麻与硬性毒品使用之间的观测关联。