The classification permutation test: A flexible approach to testing for covariate imbalance in observational studies
| Autor: | Johann A. Gagnon-Bartsch, Yotam Shem-Tov |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Balance Matching (statistics) Computer science business.industry Random assignment matching Nonparametric statistics Inference Machine learning computer.software_genre Random forest Permutation Modeling and Simulation Resampling Covariate observational study Artificial intelligence Statistics Probability and Uncertainty business computer natural experiment |
| Zdroj: | Ann. Appl. Stat. 13, no. 3 (2019), 1464-1483 |
| Popis: | The gold standard for identifying causal relationships is a randomized controlled experiment. In many applications in the social sciences and medicine, the researcher does not control the assignment mechanism and instead may rely upon natural experiments or matching methods as a substitute to experimental randomization. The standard testable implication of random assignment is covariate balance between the treated and control units. Covariate balance is commonly used to validate the claim of as good as random assignment. We propose a new nonparametric test of covariate balance. Our Classification Permutation Test (CPT) is based on a combination of classification methods (e.g., random forests) with Fisherian permutation inference. We revisit four real data examples and present Monte Carlo power simulations to demonstrate the applicability of the CPT relative to other nonparametric tests of equality of multivariate distributions. |
| Databáze: | OpenAIRE |
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