Abstract
Summary We construct tests for the null hypothesis that the conditional average treatment effect is non-negative, conditional on every possible value of a subset of covariates. Testing such a null hypothesis can provide more information than the sign of the average treatment effects parameter. The null hypothesis can be characterized as infinitely many of unconditional moment inequalities. A Kolmogorov–Smirnov test is constructed based on these unconditional moment inequalities, and a simulated critical value is proposed. It is shown that our test can control the size uniformly over a broad set of data-generating processes asymptotically, that it is consistent against fixed alternatives and that it is unbiased against some N−1/2 local alternatives. Several extensions of our test are also considered and we apply our tests to examine the effect of a job-training programme on real earnings.
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