论文标题
估计使用贝叶斯树合奏的连续暴露的异质效果：重新审视堕胎率对犯罪的影响
Estimating heterogeneous effects of continuous exposures using Bayesian tree ensembles: revisiting the impact of abortion rates on crime
论文作者
论文摘要
在估计连续暴露或治疗的因果作用时，控制所有混杂因素很重要。但是，大多数现有方法都需要参数规范，以控制控制变量如何影响结果或广义倾向得分，并且对治疗效果的推断通常对此选择敏感。此外，通常是估计治疗效果在观察单元之间如何变化的目标。为了解决这一差距，我们提出了使用贝叶斯树的共同体估算连续治疗暴露的因果关系的半参数模型，该曝光的因果关系不需要（i）不需要对控制变量影响的先验参数规范，并且（ii）允许通过预测的调制器鉴定效果修饰。我们做出的主要参数假设是，暴露对结果的影响是线性的，这种关系的陡度由主持人的非参数函数确定，我们提供了启发式方法来诊断该假设的有效性。我们应用方法来重新审视2001年的研究，该研究如何影响犯罪发生率。
In estimating the causal effect of a continuous exposure or treatment, it is important to control for all confounding factors. However, most existing methods require parametric specification for how control variables influence the outcome or generalized propensity score, and inference on treatment effects is usually sensitive to this choice. Additionally, it is often the goal to estimate how the treatment effect varies across observed units. To address this gap, we propose a semiparametric model using Bayesian tree ensembles for estimating the causal effect of a continuous treatment of exposure which (i) does not require a priori parametric specification of the influence of control variables, and (ii) allows for identification of effect modification by pre-specified moderators. The main parametric assumption we make is that the effect of the exposure on the outcome is linear, with the steepness of this relationship determined by a nonparametric function of the moderators, and we provide heuristics to diagnose the validity of this assumption. We apply our methods to revisit a 2001 study of how abortion rates affect incidence of crime.
