Learning control variables and instruments for causal analysis in observational data
University of Innsbruck（因斯布鲁克大学）；Centre for Health Economics（健康经济中心）；University of Fribourg（弗里堡大学）；Heinrich Heine University Düsseldorf（杜塞尔多夫海因里希·海涅大学）
Nicolas Apfel, Julia Hatamyar, Martin Huber, Jannis Kueck
This study introduces a data-driven, machine learning-based method to detect suitable control variables and instruments for assessing the causal effect of a treatment on an outcome in observational data. Our approach tests the joint existence of instruments, which are associated with the treatment but not directly with the outcome (at least conditional on observables), and suitable control variables, conditional on which the treatment is exogenous, and learns the partition of instruments and control variables from the observed data. The detection of sets of instruments and control variables relies on the condition that proper instruments are conditionally independent of the outcome given the treatment and suitable control variables. We establish the consistency of our method for detecting control variables and instruments under certain regularity conditions, investigate the finite sample performance through a simulation study, and provide an empirical application to health data from the Oregon Health Insurance Experiment.
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
Boston University（波士顿大学）；Stanford University（斯坦福大学）；University of California, Irvine（加州大学尔湾分校）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Pennsylvania（宾夕法尼亚大学）
本文融合存在未观测混杂的观测数据与仅有短期随访的实验数据，提出三类长期因果效应识别假设及基于影响函数的估计方法，并以班级规模对 SAT 分数的影响进行实证验证。
