Estimation of local treatment effects under the binary instrumental variable model
Estimation of local treatment effects under the binary instrumental variable model
复制标题
二元工具变量模型下的局部治疗效果估计
作者：
Linbo Wang;Yuexia Zhang;T. Richardson;J. Robins
在观察性研究和不完全随机对照试验中，工具变量被广泛用于处理未测量的混杂。在这些研究中，研究人员经常针对所谓的局部平均治疗效果，因为它在温和的条件下是可以识别的。本文考虑了二元辅助变量模型下局部平均处理效应的估计问题。我们讨论了具有二元结果的因果估计的挑战，并表明，令人惊讶的是，它可能比在具有连续结果的情况下更困难。我们提出了新的建模和估计方法，在模型的适宜性、可解释性、稳健性和效率方面改进了现有的建议。通过仿真研究和实际数据分析，说明了我们的方法。
Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges of causal estimation with a binary outcome and show that, surprisingly, it can be more difficult than in the case with a continuous outcome. We propose novel modelling and estimation procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness and efficiency. Our approach is illustrated via simulation studies and a real data analysis.