论文标题
校准后倾向分数模型更好
Propensity score models are better when post-calibrated
论文作者
论文摘要
使用倾向分数的因果推断的理论保证部分是基于表现如条件概率的得分。但是，零和一个之间的分数，尤其是在通过灵活的统计估计器输出时，并不一定像概率一样。我们进行了一项仿真研究，以评估估计在应用简单且完善的后加工方法校准倾向得分之前和之后估计平均治疗效果的错误。我们发现，校准后降低了表达未校准的统计估计器的误差估计，并且这种改进不是通过更好的平衡来介导的。最初缺乏校准的越大，效果估计的改进就越大，对已经校准的估计器的影响很小。鉴于有效的估计有所改善，并且校准在计算上很便宜，因此我们建议在用表达模型对倾向得分进行建模时将其采用。
Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities. However, scores between zero and one, especially when outputted by flexible statistical estimators, do not necessarily behave like probabilities. We perform a simulation study to assess the error in estimating the average treatment effect before and after applying a simple and well-established post-processing method to calibrate the propensity scores. We find that post-calibration reduces the error in effect estimation for expressive uncalibrated statistical estimators, and that this improvement is not mediated by better balancing. The larger the initial lack of calibration, the larger the improvement in effect estimation, with the effect on already-calibrated estimators being very small. Given the improvement in effect estimation and that post-calibration is computationally cheap, we recommend it will be adopted when modelling propensity scores with expressive models.
