Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
对弱工具变量或无效工具变量的稳健性:利用机器学习探索非线性治疗模型
We discuss causal inference for observational studies with possibly invalid instrumental variables. We propose a novel methodology called two-stage curvature identification (TSCI) by exploring the nonlinear treatment model with machine learning. {The first-stage machine learning enables improving the instrumental variable's strength and adjusting for different forms of violating the instrumental variable assumptions.} The success of TSCI requires the instrumental variable's…