Regression and weighting methods for causal inference using instrumental variables
Regression and weighting methods for causal inference using instrumental variables
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DOI：
10.1198/016214505000001366
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发表时间：
2006-12-01
影响因子：
3.7
通讯作者：
Tan, Zhiqiang
中科院分区：
文献类型：
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作者：
Tan, Zhiqiang
近年来，计量经济学和统计学的研究对使用工具变量进行因果推断有了相当深入的了解。一个基本的想法是，静脉注射作为一个实验手柄，它的转动可能会改变每个人的治疗状态，并通过和只有通过这种效果，也改变观察到的结果。观察到的结果相对于治疗状态的平均差异给出了在该假设实验中治疗状态改变的那些人的平均治疗效果。我们建立在现代IV框架，并开发了两种估计方法，并行回归调整和倾向评分加权的情况下，治疗选择的基础上协变量。IV假设明确以协变量为条件，以考虑到工具可能与这些背景变量相关的事实。回归方法侧重于缓解（观察到的结局和治疗状态）与针对协变量调整的工具之间的关系。加权法侧重于工具和协变量之间的关系，以平衡不同工具组与协变量之间的关系。对于这两种方法，建模假设是直接对观测数据和分离的IV假设，而因果关系的推断相结合的adviseddata模型与IV假设，通过识别结果。这种方法非常简单且灵活，足以容纳各种参数和半参数技术，这些技术试图从观察到的数据中学习关联关系。我们举例说明的方法，估计教育回报的应用程序。
Recent researches in econometrics and statistics have gained considerable insights into the use of instrumental variables (lVs) for causal inference. A basic idea is that IVs serve as an experimental handle, the turning of which may change each individual's treatment status and, through and only through this effect, also change observed outcome. The average difference in observed outcome relative to that in treatment status gives the average treatment effect for those whose treatment status is changed in this hypothetical experiment. We build on the modern IV framework and develop two estimation methods in parallel to regression adjustment and propensity score weighting in the case of treatment selection based on covariates. The IV assumptions are made explicitly conditional on covariates to allow for the fact that instruments can be related to these background variables. The regression method focuses on the relationship between responses (observed outcome and treatment status jointly) and instruments adjusted for covariates. The weighting method focuses on the relationship between instruments and covariates to balance different instrument groups with respect to covariates. For both methods, modeling assumptions are made directly on observed data and separated from the IV assumptions, whereas causal effects are inferred by combining observeddata models with the IV assumptions through identification results. This approach is straightforward and flexible enough to host various parametric and serniparametric techniques that attempt to learn associational relationships from observed data. We illustrate the methods by an application to estimating returns to education.