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Confounder selection is perhaps the most important step in the design of observational studies.
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2011
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2017
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[author] Chernozhukov, VictorV., Chetverikov, DenisD., Demirer, MertM., Duflo, EstherE., Hansen, ChristianC., Newey, WhitneyW. and Robins, JamesJ. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal 21 C1-C68. 10.1111/ectj.12097
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[author] Richardson, Thomas S.T. S., Robins, James M.J. M. and Wang, LinboL. (2018). Discussion of “Data-driven confounder selection via Markov and Bayesian networks” by Häggström. Biometrics 74 403-406
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[author] Strobl, Eric VE. V., Spirtes, Peter LP. L. and Visweswaran, ShyamS. (2019). Estimating and controlling the false discovery rate of the PC algorithm using edge-specific p-values. ACM Transactions on Intelligent Systems and Technology (TIST) 10 1–37
2019
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[author] VanderWeele, Tyler JT. J. (2019). Principles of confounder selection. European Journal of Epidemiology 34 211–219
2019
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[author] Witte, JanineJ. and Didelez, VanessaV. (2019). Covariate selection strategies for causal inference: Classification and comparison. Biometrical Journal 61 1270–1289
2019
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[author] Hernán, M. A.M. A. and Robins, J. M.J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC, Boca Raton
2020
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[author] Koch, BrandonB., Vock, David MD. M., Wolfson, JulianJ. and Vock, Laura BoehmL. B. (2020). Variable selection and estimation in causal inference using Bayesian spike and slab priors. Statistical Methods in Medical Research 29 2445-2469. 10.1177/0962280219898497
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[author] Rotnitzky, AndreaA. and Smucler, EzequielE. (2020). Efficient adjustment sets for population average causal treatment effect estimation in graphical models. Journal of Machine Learning Research 21 1–86
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[author] Witte, JanineJ., Henckel, LeonardL., Maathuis, Marloes H.M. H. and Didelez, VanessaV. (2020). On efficient adjustment in causal graphs. Journal of Machine Learning Research 21 246
2020
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[author] Loh, Wen WeiW. W. and Vansteelandt, StijnS. (2021). Confounder selection strategies targeting stable treatment effect estimators. Statistics in Medicine 40 607-630
2021
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[author] Guo, F RichardF. R., Perković, EmilijaE. and Rotnitzky, AndreaA. (2022). Variable elimination, graph reduction and the efficient g-formula. Biometrika 110 739-761. 10.1093/biomet/asac062
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[author] Henckel, LeonardL., Perković, EmilijaE. and Maathuis, Marloes H.M. H. (2022). Graphical criteria for efficient total effect estimation via adjustment in causal linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 84 579-599
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[author] Shpitser, IlyaI., Richardson, Thomas S.T. S. and Robins, James M.J. M. (2022). Multivariate counterfactual systems and causal graphical models, In Probabilistic and Causal Inference: The Works of Judea Pearl 1 ed. 813–852. Association for Computing Machinery, New York, NY, USA
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[author] Smucler, EzequielE. and Rotnitzky, AndreaA. (2022). A note on efficient minimum cost adjustment sets in causal graphical models. Journal of Causal Inference 10 174–189
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[author] Tang, DingkeD., Kong, DehanD., Pan, WenliangW. and Wang, LinboL. (2022). Ultra-high dimensional variable selection for doubly robust causal inference. Biometrics 79 903–914
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