Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot
Author:
Affiliation:
1. Division of Biostatistics, Berkeley, CA 94720
2. Kaiser Permanente Northern California Division of Research, Oakland, CA 94612
3. Department of Statistics, Berkeley, CA 94720
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,General Mathematics,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/00031305.2024.2303419
Reference50 articles.
1. Abadie A. Chingos M. M. and West M. R. (2018) “Endogenous stratification in randomized experiments ” Review of Economics and Statistics 100 567–580.
2. Agarwal A. Kenney A. M. Tan Y. S. Tang T. M. and Yu B. (2023) “MDI+: A Flexible Random Forest-Based Feature Importance Framework ” arXiv preprint arXiv:2307.01932 .
3. Ahmed A. Husain A. Love T. E. Gambassi G. Dell’Italia L. J. Francis G. S. Gheorghiade M. Allman R. M. Meleth S. and Bourge R. C. (2006) “Heart failure chronic diuretic use and increase in mortality and hospitalization: an observational study using propensity score methods ” European heart journal 27 1431–1439.
4. Aikens R. C. and Baiocchi M. (2022) “Assignment-Control Plots: A Visual Companion for Causal Inference Study Design ” The American Statistician 1–13.
5. Aikens R. C. Greaves D. and Baiocchi M. (2020) “A pilot design for observational studies: Using abundant data thoughtfully ” Statistics in Medicine 39 4821–4840.
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