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Literature DB >> 29744711

You are smarter than you think: (super) machine learning in context.

Alexander P Keil¹, Jessie K Edwards².

Abstract

Entities: Chemical Disease Gene Species

Mesh：

Year: 2018 PMID： 29744711 PMCID： PMC6226364 DOI： 10.1007/s10654-018-0405-9

Source DB: PubMed Journal: Eur J Epidemiol ISSN： 0393-2990 Impact factor: 8.082

× No keyword cloud information.

8 in total

1. Data, design, and background knowledge in etiologic inference.

Authors: J M Robins
Journal: Epidemiology Date: 2001-05 Impact factor: 4.822

Review 2. Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies.

Authors: Sander Greenland
Journal: Am J Epidemiol Date: 2004-08-15 Impact factor: 4.897

3. An empirical comparison of tree-based methods for propensity score estimation.

Authors: Stephanie Watkins; Michele Jonsson-Funk; M Alan Brookhart; Steven A Rosenberg; T Michael O'Shea; Julie Daniels
Journal: Health Serv Res Date: 2013-05-23 Impact factor: 3.402

4. Estimating predicted probabilities from logistic regression: different methods correspond to different target populations.

Authors: Clemma J Muller; Richard F MacLehose
Journal: Int J Epidemiol Date: 2014-03-05 Impact factor: 7.196

5. Multivariate estimation of exposure-specific incidence from case-control studies.

Authors: S Greenland
Journal: J Chronic Dis Date: 1981

6. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

Authors: Daniel Westreich; Justin Lessler; Michele Jonsson Funk
Journal: J Clin Epidemiol Date: 2010-08 Impact factor: 6.437

7. Improving propensity score weighting using machine learning.

Authors: Brian K Lee; Justin Lessler; Elizabeth A Stuart
Journal: Stat Med Date: 2010-02-10 Impact factor: 2.373

Review 8. Stacked generalization: an introduction to super learning.

Authors: Ashley I Naimi; Laura B Balzer
Journal: Eur J Epidemiol Date: 2018-04-10 Impact factor: 8.082

8 in total

6 in total

6. Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions.

Authors: Alex Broadbent; Thomas Grote
Journal: Philos Technol Date: 2022-02-26

6 in total

You are smarter than you think: (super) machine learning in context.

1. Data, design, and background knowledge in etiologic inference.

Review 2. Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies.

3. An empirical comparison of tree-based methods for propensity score estimation.

4. Estimating predicted probabilities from logistic regression: different methods correspond to different target populations.

5. Multivariate estimation of exposure-specific incidence from case-control studies.

6. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

7. Improving propensity score weighting using machine learning.

Review 8. Stacked generalization: an introduction to super learning.

1. On the relationship of machine learning with causal inference.

2. Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!).

3. Machine Learning for Causal Inference: On the Use of Cross-fit Estimators.

4. Public health application of predictive modeling: an example from farm vehicle crashes.

5. G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes.

6. Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions.