Zhang R, Shen L, Miles T, et al. Association of low to moderate alcohol drinking with cognitive functions from middle to older age among US adults. JAMA Netw Open. 2020;3(6): e207922. https://doi.org/10.1001/jamanetworkopen.2020.7922.

Article  PubMed  PubMed Central  Google Scholar 

Jeon KH, Han K, Jeong SM, et al. Changes in alcohol consumption and risk of dementia in a nationwide cohort in South Korea. JAMA Netw Open. 2023;6(2): e2254771. https://doi.org/10.1001/jamanetworkopen.2022.54771.

Article  PubMed  Google Scholar 

Bell S, Daskalopoulou M, Rapsomaniki E, et al. Association between clinically recorded alcohol consumption and initial presentation of 12 cardiovascular diseases: population based cohort study using linked health records. BMJ. 2017;356: j909. https://doi.org/10.1136/bmj.j909.

Article  PubMed  PubMed Central  Google Scholar 

Mitchell G, Lesch M, McCambridge J. Alcohol industry involvement in the moderate alcohol and cardiovascular health trial. Am J Public Health. 2020;110(4):485–8. https://doi.org/10.2105/AJPH.2019.305508.

Article  PubMed  PubMed Central  Google Scholar 

Lawlor DA, Tilling K, Davey SG. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45(6):1866–86. https://doi.org/10.1093/ije/dyw314.

Article  PubMed  Google Scholar 

Matthay EC, Hagan E, Gottlieb LM, et al. Alternative causal inference methods in population health research: evaluating tradeoffs and triangulating evidence. SSM - Popul Health. 2020;10: 100526. https://doi.org/10.1016/j.ssmph.2019.100526.

Article  PubMed  Google Scholar 

Lash TL, VanderWeele TJ, Haneuse S, Rothman KJ. Modern epidemiology. 4th ed. Wolters Kluwer/Lippincott Williams & Wilkins; 2021.

Google Scholar 

Lawlor DA, Davey Smith G, Kundu D, Bruckdorfer KR, Ebrahim S. Those confounded vitamins: What can we learn from the differences between observational versus randomised trial evidence? Lancet. 2004;363(9422):1724–7. https://doi.org/10.1016/S0140-6736(04)16260-0.

Article  PubMed  Google Scholar 

Digitale JC, Martin JN, Glymour MM. Tutorial on directed acyclic graphs. J Clin Epidemiol. 2022;142:264–7. https://doi.org/10.1016/j.jclinepi.2021.08.001.

Article  PubMed  Google Scholar 

Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37–48.

Article  CAS  PubMed  Google Scholar 

Egger M, Higgins JPT, Davey Smith G (eds) Systematic Reviews in Health Research: Meta-Analysis in Context. Wiley; 2022. https://doi.org/10.1002/9781119099369.ch1

Deaton A, Cartwright N. Understanding and misunderstanding randomized controlled trials. Soc Sci Med. 1982;2018(210):2–21. https://doi.org/10.1016/j.socscimed.2017.12.005.

Article  Google Scholar 

Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(4):722–9. https://doi.org/10.1093/ije/29.4.722.

Article  CAS  PubMed  Google Scholar 

Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S. Good practices for quantitative bias analysis. Int J Epidemiol. 2014;43(6):1969–85. https://doi.org/10.1093/ije/dyu149.

Article  PubMed  Google Scholar 

Sanderson E, Glymour MM, Holmes MV, et al. Mendelian randomization. Nat Rev Methods Primer. 2022;2:6. https://doi.org/10.1038/s43586-021-00092-5.

Article  CAS  Google Scholar 

Lipsitch M, Tchetgen ET, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology. 2010;21(3):383–8. https://doi.org/10.1097/EDE.0b013e3181d61eeb.

Article  PubMed  PubMed Central  Google Scholar 

Hernán MA, Robins JM. Causal inference: what if. Chapman & Hall/CRC Press; 2024.

Google Scholar 

Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615–25. https://doi.org/10.1097/01.ede.0000135174.63482.43.

Article  PubMed  Google Scholar 

Davey Smith G, Richmond R, Pingault JB (eds) Combining Human Genetics and Causal Inference to Understand Human Disease and Development. Cold Spring Harbor Laboratory Press; 2022.

Cook TD. Twenty-six assumptions that have to be met if single random assignment experiments are to warrant “gold standard” status: a commentary on Deaton and Cartwright. Soc Sci Med. 1982;2018(210):37–40. https://doi.org/10.1016/j.socscimed.2018.04.031.

Article  Google Scholar 

Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–55. https://doi.org/10.1080/01621459.1996.10476902.

Article  Google Scholar 

Newhouse JP, McClellan M. Econometrics in outcomes research: the use of instrumental variables. Annu Rev Public Health. 1998;19:17–34. https://doi.org/10.1146/annurev.publhealth.19.1.17.

Article  CAS  PubMed  Google Scholar 

Allen N, Sudlow C, Downey P, et al. UK Biobank: current status and what it means for epidemiology. Health Policy Technol. 2012;1(3):123–6. https://doi.org/10.1016/j.hlpt.2012.07.003.

Article  Google Scholar 

Davey Smith G, Ebrahim S. ‘Mendelian randomization’: Can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1–22. https://doi.org/10.1093/ije/dyg070.

Article  Google Scholar 

Palmer TM, Lawlor DA, Harbord RM, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res. 2012;21(3):223–42. https://doi.org/10.1177/0962280210394459.

Article  PubMed  PubMed Central  Google Scholar 

Pierce BL, Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013;178(7):1177–84. https://doi.org/10.1093/aje/kwt084.

Article  PubMed  PubMed Central  Google Scholar 

Millwood IY, Walters RG, Mei XW, et al. Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China. Lancet. 2019;393(10183):1831–42. https://doi.org/10.1016/S0140-6736(18)31772-0.

Article  PubMed  PubMed Central  Google Scholar 

Hamilton FW, Hughes DA, Spiller W, Tilling K, Davey SG. Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol. Eur J Epidemiol. 2024. https://doi.org/10.1007/s10654-024-01113-9.

Article  PubMed  PubMed Central  Google Scholar 

Power GM, Sanderson E, Pagoni P, et al. Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: a systematic literature review. Eur J Epidemiol. 2024;39(5):501–20. https://doi.org/10.1007/s10654-023-01032-1.

Article  PubMed  Google Scholar 

Caspi A, Moffitt TE, Morgan J, et al. Maternal expressed emotion predicts children’s antisocial behavior problems: using monozygotic-twin differences to identify environmental effects on behavioral development. Dev Psychol. 2004;40(2):149–61. https://doi.org/10.1037/0012-1649.40.2.149.

Article  PubMed  Google Scholar 

D’Onofrio BM, Turkheimer EN, Eaves LJ, et al. The role of the children of twins design in elucidating causal relations between parent characteristics and child outcomes. J Child Psychol Psychiatry. 2003;44(8):1130–44. https://doi.org/10.1111/1469-7610.00196.

Article  PubMed  Google Scholar 

Brion MJA, Lawlor DA, Matijasevich A, et al. What are the causal effects of breastfeeding on IQ, obesity and blood pressure? Evidence from comparing high-income with middle-income cohorts. Int J Epidemiol. 2011;40(3):670–80. https://doi.org/10.1093/ije/dyr020.

Article  PubMed  PubMed Central  Google Scholar 

Desai JR, Hyde CL, Kabadi S, et al. Utilization of positive and negative controls to examine comorbid associations in observational database studies. Med Care. 2017;55(3):244–51. https://doi.org/10.1097/MLR.0000000000000640.

Article  PubMed  Google Scholar 

Lu H, Cole SR, Howe CJ, Westreich D. Toward a clearer definition of selection bias when estimating causal effects. Epidemiol Camb Mass. 2022;33(5):699. https://doi.org/10.1097/EDE.0000000000001516.

Article