Controlling false positive selections in high-dimensional regression and causal inference
| Autor: | Peter Bühlmann, Markus Kalisch, Philipp Rütimann |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Statistics and Probability
Epidemiology Sample (statistics) Expected value Machine learning computer.software_genre 01 natural sciences 010104 statistics & probability 03 medical and health sciences Health Information Management Lasso (statistics) Statistics False positive paradox False Positive Reactions 0101 mathematics High dimensional regression 030304 developmental biology Mathematics 0303 health sciences Models Statistical business.industry Regression Causality Causal inference Observational study Artificial intelligence business computer |
| Zdroj: | Statistical Methods in Medical Research Online First |
| ISSN: | 1477-0334 |
| Popis: | Guarding against false positive selections is important in many applications. We discuss methods based on subsampling and sample splitting for controlling the expected number of false positives and assigning p values. They are generic and especially useful for high dimensional settings. We review encouraging results for regression and we discuss new adaptations and remaining challenges for selecting relevant variables based on observational data having a causal or interventional effect on a response of interest. Keywords high dimensional causal inference high dimensional regression Lasso observational data p values PC algorithm stability selection |
| Databáze: | OpenAIRE |
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