Propensity Score-Matching Methods for Nonexperimental Causal Studies
讨论在非实验环境中使用倾向得分匹配方法处理样本选择偏差，并用国家支持工作实验数据验证该方法能有效减少处理组与对照组系统差异带来的偏差。
This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pretreatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units. © 2002 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology