A Review of Causal Inference for Biomedical Informatics

Overview

Journal J Biomed Inform

Publisher Elsevier

Specialty Medical Informatics

Date 2011 Jul 26

PMID 21782035

Citations 36

Authors

Samantha Kleinberg

George Hripcsak

Affiliations

Soon will be listed here.

Abstract

Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods.

Citing Articles

Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.

Wu H, Shi W, Wang M BMC Med Inform Decis Mak. 2024; 24(1):137.

PMID: 38802809 PMC: 11129385. DOI: 10.1186/s12911-024-02510-6.

Opportunities for incorporating intersectionality into biomedical informatics.

Bear Dont Walk 4th O, Paullada A, Everhart A, Casanova-Perez R, Cohen T, Veinot T J Biomed Inform. 2024; 154:104653.

PMID: 38734158 PMC: 11146624. DOI: 10.1016/j.jbi.2024.104653.

Assessing the effect of () (Linnaeus, 1762) control based on machine learning for predicting the spatiotemporal distribution of eggs in ovitraps.

Piovezan R, de Azevedo T, Faria E, Veroneze R, Von Zuben C, Von Zuben F Dialogues Health. 2024; 1:100003.

PMID: 38515905 PMC: 10954012. DOI: 10.1016/j.dialog.2022.100003.

Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.

Zhang H, Jethani N, Jones S, Genes N, Major V, Jaffe I medRxiv. 2024; .

PMID: 38405784 PMC: 10888985. DOI: 10.1101/2023.07.10.23292373.

A Comprehensive Framework for Measuring the Immediate Impact of TV Advertisements: TV-Impact.

Arslan A, Tecimer K, Turgut H, Bali O, Yucel A, Alptekin G Entropy (Basel). 2024; 26(2).

PMID: 38392364 PMC: 10887522. DOI: 10.3390/e26020109.

References

Cartwright N, Munro E . The limitations of randomized controlled trials in predicting effectiveness. J Eval Clin Pract. 2010; 16(2):260-6. DOI: 10.1111/j.1365-2753.2010.01382.x. View

Ward A . Causal criteria and the problem of complex causation. Med Health Care Philos. 2009; 12(3):333-43. DOI: 10.1007/s11019-009-9182-2. View

Zou M, Conzen S . A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics. 2004; 21(1):71-9. DOI: 10.1093/bioinformatics/bth463. View

Mukhopadhyay N, Chatterjee S . Causality and pathway search in microarray time series experiment. Bioinformatics. 2006; 23(4):442-9. DOI: 10.1093/bioinformatics/btl598. View

Mani S, Cooper G . Causal discovery using a Bayesian local causal discovery algorithm. Stud Health Technol Inform. 2004; 107(Pt 1):731-5. View

Hofler M . The Bradford Hill considerations on causality: a counterfactual perspective. Emerg Themes Epidemiol. 2005; 2:11. PMC: 1291382. DOI: 10.1186/1742-7622-2-11. View

Wu J, Roy J, Stewart W . Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med Care. 2010; 48(6 Suppl):S106-13. DOI: 10.1097/MLR.0b013e3181de9e17. View

Thygesen L, Andersen G, Andersen H . A philosophical analysis of the Hill criteria. J Epidemiol Community Health. 2005; 59(6):512-6. PMC: 1757057. DOI: 10.1136/jech.2004.027524. View

Parascandola M, Weed D . Causation in epidemiology. J Epidemiol Community Health. 2001; 55(12):905-12. PMC: 1731812. DOI: 10.1136/jech.55.12.905. View

10.

Russo F . Variational causal claims in epidemiology. Perspect Biol Med. 2009; 52(4):540-54. DOI: 10.1353/pbm.0.0118. View

11.

KARHAUSEN L . Causation: the elusive grail of epidemiology. Med Health Care Philos. 2000; 3(1):59-67. DOI: 10.1023/a:1009970730507. View

12.

Rothman K, Greenland S . Causation and causal inference in epidemiology. Am J Public Health. 2005; 95 Suppl 1:S144-50. DOI: 10.2105/AJPH.2004.059204. View

13.

Gaziano J . The evolution of population science: advent of the mega cohort. JAMA. 2010; 304(20):2288-9. DOI: 10.1001/jama.2010.1691. View

14.

Rothwell P . External validity of randomised controlled trials: "to whom do the results of this trial apply?". Lancet. 2005; 365(9453):82-93. DOI: 10.1016/S0140-6736(04)17670-8. View

15.

Naranjo C, Busto U, Sellers E, Sandor P, Ruiz I, Roberts E . A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther. 1981; 30(2):239-45. DOI: 10.1038/clpt.1981.154. View

16.

Xiang Z, Minter R, Bi X, Woolf P, He Y . miniTUBA: medical inference by network integration of temporal data using Bayesian analysis. Bioinformatics. 2007; 23(18):2423-32. DOI: 10.1093/bioinformatics/btm372. View

17.

Rothwell P . Factors that can affect the external validity of randomised controlled trials. PLoS Clin Trials. 2006; 1(1):e9. PMC: 1488890. DOI: 10.1371/journal.pctr.0010009. View

18.

Suermondt H, Cooper G . An evaluation of explanations of probabilistic inference. Proc Annu Symp Comput Appl Med Care. 1992; :579-85. PMC: 2248083. View

19.

Hill A . THE ENVIRONMENT AND DISEASE: ASSOCIATION OR CAUSATION?. Proc R Soc Med. 1965; 58:295-300. PMC: 1898525. DOI: 10.1177/003591576505800503. View

20.

Long W . Temporal reasoning for diagnosis in a causal probabilistic knowledge base. Artif Intell Med. 1996; 8(3):193-215. DOI: 10.1016/0933-3657(95)00033-x. View