Unobserved heterogeneous treatment effects have been emphasized in the recent policy evaluation literature (see, e.g., Heckman and Vytlacil (2005, Econometrica 73, 669–738)). This paper proposes a nonparametric test for unobserved heterogeneous treatment effects in a treatment effect model with a binary treatment assignment, allowing for individuals’ self-selection to the treatment. Under the standard local average treatment effects assumptions, i.e., the no defiers condition, we derive testable model restrictions for the hypothesis of unobserved heterogeneous treatment effects. Furthermore, we show that if the treatment outcomes satisfy a monotonicity assumption, these model restrictions are also sufficient. Then, we propose a modified Kolmogorov–Smirnov-type test which is consistent and simple to implement. Monte Carlo simulations show that our test performs well in finite samples. For illustration, we apply our test to study heterogeneous treatment effects of the Job Training Partnership Act on earnings and the impacts of fertility on family income, where the null hypothesis of homogeneous treatment effects gets rejected in the second case but fails to be rejected in the first application.

最近的政策评估文献强调了未观察到的异质性治疗效果（例如，参见 Heckman 和 Vytlacil (2005, Econometrica 73, 669–738)）。本文提出了一种非参数检验，用于在具有二元治疗分配的治疗效果模型中检测未观察到的异质治疗效果，允许个人自行选择治疗。标准下局部平均处理效果假设，即无违约条件，我们为未观察到的异质治疗效果的假设推导出可测试的模型限制。此外，我们表明，如果治疗结果满足单调性假设，则这些模型限制也是足够的。然后，我们提出了一种修改后的 Kolmogorov–Smirnov 型检验，该检验具有一致性且易于实施。蒙特卡洛模拟表明我们的测试在有限样本中表现良好。为了说明，我们应用我们的检验来研究《职业培训合作法》对收入的异质处理效果以及生育率对家庭收入的影响，其中同质处理效果的原假设在第二种情况下被拒绝，但在第二种情况下未能被拒绝第一次申请。