Inferring causal effects of continuous-valued treatments from observational data is a crucial task promising to better inform policy- and decision-makers. A critical assumption needed to identify these effects is that all confounding variables -- causal parents of both the treatment and the outcome -- are included as covariates. Unfortunately, given observational data alone, we cannot know with certainty that this criterion is satisfied. Sensitivity analyses provide principled ways to give bounds on causal estimates when confounding variables are hidden. While much attention is focused on sensitivity analyses for discrete-valued treatments, much less is paid to continuous-valued treatments. We present novel methodology to bound both average and conditional average continuous-valued treatment-effect estimates when they cannot be point identified due to hidden confounding. A semi-synthetic benchmark on multiple datasets shows our method giving tighter coverage of the true dose-response curve than a recently proposed continuous sensitivity model and baselines. Finally, we apply our method to a real-world observational case study to demonstrate the value of identifying dose-dependent causal effects.

翻译：从观测数据推断连续型处理的因果效应是一项关键任务，有望更好地为政策和决策制定者提供信息。识别这些效应所需的一个关键假设是，所有混杂变量（即处理和结果的共同原因）都作为协变量包含在内。不幸的是，仅凭观测数据，我们无法确定这一标准是否满足。敏感性分析提供了在混杂变量隐藏时对因果估计给出界的原则性方法。尽管大量研究关注离散型处理的敏感性分析，但对连续型处理的关注却少得多。我们提出了一种新颖的方法，用于在因隐藏混杂而无法点识别时，对平均和条件平均连续型处理效应估计进行边界界定。在多个数据集上的半合成基准测试表明，我们的方法比最近提出的连续敏感性模型和基线方法更紧密地覆盖了真实的剂量反应曲线。最后，我们将该方法应用于真实世界的观测案例研究，以证明识别剂量依赖性因果效应的价值。