Bayesian Model Averaging in Causal Instrumental Variable Models
提出gIVBMA方法，通过贝叶斯模型平均自动选择有效工具变量和协变量，增强对无效工具的稳健性，适用于非高斯数据，并在模拟和两个实证应用中优于现有方法。
ABSTRACT Instrumental variables are a popular tool to infer causal effects under unobserved confounding, but choosing suitable instruments is challenging in practice. We propose gIVBMA, a Bayesian model averaging procedure that addresses this challenge by averaging across different sets of instrumental variables and covariates in a structural equation model. This allows for data‐driven selection of valid and relevant instruments and provides additional robustness against invalid instruments. Our approach extends previous work through a scale‐invariant prior structure and accommodates non‐Gaussian outcomes and treatments, offering greater flexibility than existing methods. The computational strategy uses conditional Bayes factors to update models separately for the outcome and treatments. We prove that this model selection procedure is consistent. In simulation experiments, gIVBMA outperforms current state‐of‐the‐art methods. We demonstrate its usefulness in two empirical applications: the effects of malaria and institutions on income per capita and the returns to schooling. A software implementation of gIVBMA is available in Julia.