A simple test for the ignorability of non-compliance in experiments
This papers proposes a simple method for testing whether non-compliance in experiments is ignorable, i.e., not jointly related to the treatment and the outcome. The approach consists of (i) regressing the outcome variable on a constant, the treatment, the assignment indicator, and the treatment/assignment interaction and (ii) testing whether the coefficients on the latter two variables are jointly equal to zero. A brief simulation study illustrates the finite sample properties of the test.
|Date of creation:||Apr 2013|
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- Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2014. "Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 395-415, July.
- Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2012. "Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT," IEAS Working Paper : academic research 12-A017, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Jan 2014.
- de Luna, Xavier & Johansson, Per, 2012.
"Testing for Nonparametric Identification of Causal Effects in the Presence of a Quasi-Instrument,"
IZA Discussion Papers
6692, Institute for the Study of Labor (IZA).
- de Luna, Xavier & Johansson, Per, 2012. "Testing for nonparametric identification of causal effects in the presence of a quasi-instrument," Working Paper Series 2012:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.
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