Conditional effect estimation has great scientific and policy importance because interventions may impact subjects differently depending on their characteristics. Most research has focused on estimating the conditional average treatment effect (CATE). However, identification of the CATE requires all subjects have a non-zero probability of receiving treatment, or positivity, which may be unrealistic in practice. Instead, we propose conditional effects based on incremental propensity score interventions, which are stochastic interventions where the odds of treatment are multiplied by some factor. These effects do not require positivity for identification and can be better suited for modeling scenarios in which people cannot be forced into treatment. We develop a projection estimator and a flexible nonparametric estimator that can each estimate all the conditional effects we propose and derive model-agnostic error guarantees showing both estimators satisfy a form of double robustness. Further, we propose a summary of treatment effect heterogeneity and a test for any effect heterogeneity based on the variance of a conditional derivative effect and derive a nonparametric estimator that also satisfies a form of double robustness. Finally, we demonstrate our estimators by analyzing the effect of intensive care unit admission on mortality using a dataset from the (SPOT)light study.

翻译：条件效应估计具有重要的科学和政策意义，因为干预措施可能因受试者特征不同而产生差异化的影响。大多数研究聚焦于估计条件平均处理效应（CATE）。然而，CATE的识别要求所有受试者具有非零的治疗分配概率（即"阳性"条件），这在实践中可能不切实际。为此，我们提出基于增量倾向得分干预的条件效应——这是一种随机干预，通过乘以某个系数调整治疗几率。这些效应无需依赖阳性条件即可识别，更适用于无法强制个体接受治疗的建模场景。我们开发了投影估计量和灵活的非参数估计量，两者均可估计我们提出的所有条件效应，并推导出模型无关的误差保证，表明这两种估计量均满足某种双重稳健性。此外，我们基于条件导数效应的方差提出了处理效应异质性的汇总指标及异质性检验，并推导出同样满足双重稳健性的非参数估计量。最后，我们利用来自（SPOT）light研究的数据集，通过分析重症监护病房入院对死亡率的影响来演示这些估计量。