Summary 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.

中文翻译：

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二元工具变量模型下局部治疗效果的估计

总结 工具变量被广泛用于处理观察性研究和不完善的随机对照试验中未测量的混杂因素。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，因为它在温和条件下是可以识别的。在本文中，我们考虑在二元工具变量模型下估计局部平均治疗效果。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，它可能比具有连续结果的情况更困难。我们提出了新颖的建模和估计程序，这些程序在模型一致性、可解释性、稳健性和效率方面改进了现有建议。我们的方法通过模拟研究和真实数据分析来说明。