Terza J
Department of Economics, Pennsylvania State University, University Park, PA 16802, USA.
Value Health. 1999 Nov-Dec;2(6):429-34. doi: 10.1046/j.1524-4733.1999.26003.x.
Treatment effect estimation is one of the mainstays of the field of outcomes research. It is, for example, a key component in analyzing the cost-effectiveness of a proposed qualitative intervention. Some outcomes researchers are hesitant to use retrospective data for treatment effect estimation because of the potential endogeneity of the treatment variable. This is unfortunate, given the abundance and other advantages of retrospective data. Others who have used retrospective data have ignored the endogeneity problem, or have not recognized its potential for causing bias in their estimates. In this paper, an econometric method that is unbiased in the presence of endogeneity and therefore broadens the potential for use of retrospective data in the estimation of treatment effects is proposed. This two-stage method is also designed to accommodate nonlinearity in the relationship between the treatment variable and the outcome. An easy to apply GAUSS implementation of the estimator is offered.
治疗效果估计是结果研究领域的主要支柱之一。例如，它是分析拟定性干预措施成本效益的关键组成部分。一些结果研究人员因治疗变量可能存在的内生性而不愿使用回顾性数据进行治疗效果估计。鉴于回顾性数据的丰富性和其他优势，这很遗憾。其他使用回顾性数据的人则忽略了内生性问题，或者没有认识到其在估计中导致偏差的可能性。本文提出了一种在存在内生性时无偏的计量经济学方法，从而扩大了回顾性数据在治疗效果估计中使用的可能性。这种两阶段方法还旨在适应治疗变量与结果之间关系的非线性。提供了该估计器易于应用的GAUSS实现。
