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Sample Empirical Likelihood Methods for Causal Inference

Jingyue Huang,Changbao Wu,Leilei Zeng

arXiv · Methodology(2024)

Cited 0|Views8
Abstract
Causal inference is crucial for understanding the true impact of interventions, policies, or actions, enabling informed decision-making and providing insights into the underlying mechanisms that shape our world. In this paper, we establish a framework for the estimation and inference of average treatment effects using a two-sample empirical likelihood function. Two different approaches to incorporating propensity scores are developed. The first approach introduces propensity scores calibrated constraints in addition to the standard model-calibration constraints; the second approach uses the propensity scores to form weighted versions of the model-calibration constraints. The resulting estimators from both approaches are doubly robust. The limiting distributions of the two sample empirical likelihood ratio statistics are derived, facilitating the construction of confidence intervals and hypothesis tests for the average treatment effect. Bootstrap methods for constructing sample empirical likelihood ratio confidence intervals are also discussed for both approaches. Finite sample performances of the methods are investigated through simulation studies.
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要点】:本文提出了一种基于两样本经验似然函数的因果推断框架,通过引入倾向得分校准约束,发展了两种估计平均处理效应的双稳健方法,并推导了相应的极限分布,用于构建置信区间和假设检验。

方法】:采用两样本经验似然函数,结合倾向得分校准约束,分别通过引入额外约束和使用加权约束两种方式,构建双稳健估计器。

实验】:通过模拟研究,探究了方法在有限样本下的表现,具体数据集名称未提及,但重点在于模拟研究的实施和结果分析。