A1273
Title: Causal inference with invalid instruments: Exploring nonlinear treatment models with machine learning
Authors: Zijian Guo - Rutgers University (United States) [presenting]
Abstract: Causal inference for observational studies with possibly invalid instrumental variables is discussed. A novel methodology called two-stage curvature identification (TSCI) is proposed, which explores the nonlinear treatment model with machine learning and adjusts for different forms of violating the instrumental variable assumptions. The success of TSCI requires the instrumental variable's effect on treatment to differ from its violation form. A novel bias correction step is implemented to remove bias resulting from the potentially high complexity of machine learning. The proposed TSCI estimator is shown to be asymptotically unbiased and normal even if the machine learning algorithm does not consistently estimate the treatment model. A data-dependent method is designed to choose the best among several candidate violation forms. TSCI is applied to study the effect of education on earnings.
