Causal inference: different target effects and their comparability_Chinese Journal of Evidence-Based Medicine

Authors：

XU Wentao , YU Tianqi , YOU Yueyuan ,  LI Jing

Research Center of Evidence-Based Medicine and Clinical Epidemiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

LI Jing, Email: lijing68@ Hotmail.com

Keywords：

Causal inference; Counterfactual; Potential outcome; Confounding; Treatment effect

DOI：

10.7507/1672-2531.202111138

Video：

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Abstract Full text Figures/Tables Video References Cited by

Causal inference is one of the main goals of medical research. However, due to the lack of an in-depth understanding of the theory of causal inference, researchers tend to blindly use multiple statistical methods to analyse the same question to enhance the credibility of the results, which leads to problems in interpretation of the analysis results. Based on the three basic concepts of potential outcomes, causal effects, and distributive mechanisms of the causal inference counterfactual framework, this paper introduced six main target effects in causal inference and discussed their comparability to help researchers understand the principle of causal inference and correctly interpret and compare research results to avoid misleading conclusions.

Citation： XU Wentao, YU Tianqi, YOU Yueyuan, LI Jing. Causal inference: different target effects and their comparability. Chinese Journal of Evidence-Based Medicine, 2022, 22(4): 490-496. doi: 10.7507/1672-2531.202111138 Copy

1.	Greenland S, Pearl J, Robins JM. Confounding and collapsibility in causal inference. Stat Sci, 1999, 14(1): 29-46.
2.	Ergina PL, Cook JA, Blazeby JM, et al. Challenges in evaluating surgical innovation. Lancet, 2009, 374(9695): 1097-1104.
3.	Lonjon G, Porcher R, Ergina P, et al. Potential pitfalls of reporting and bias in observational studies with propensity score analysis assessing a surgical procedure: a methodological systematic review. Ann Surg, 2017, 265(5): 901-909.
4.	Rosenbaum PR. Observational Studies. New York: Springer, 2002.
5.	Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
6.	Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology, 2000, 11(5): 550-560.
7.	Sato T, Matsuyama Y. Marginal structural models as a tool for standardization. Epidemiology, 2003, 14(6): 680-686.
8.	Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Stat Med, 2014, 33(13): 2297-2340.
9.	Imbens GW, Rubin DB. Causal inference in statistics, social, and biomedical sciences. Cambridge: Cambridge University Press, 2015.
10.	郭申阳. 倾向值分析: 统计方法与应用. 重庆: 重庆大学出版社, 2012.
11.	Greenland S. Randomization, statistics, and causal inference. Epidemiology, 1990, 1(6): 421-429.
12.	Greenland S, Robins JM. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol, 1986, 15(3): 413-419.
13.	Austin PC, Grootendorst P, Normand SL, et al. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Stat Med, 2007, 26(4): 754-768.
14.	Shah BR, Laupacis A, Hux JE, et al. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol, 2005, 58(6): 550-559.
15.	Austin PC. A tutorial and case study in propensity score analysis: an application to estimating the effect of in-hospital smoking cessation counseling on mortality. Multivariate Behav Res, 2011, 46(1): 119-151.
16.	Robins JM. Marginal structural models versus structural nested models as tools for causal inference. New York: Springer, 2000: 95-133.
17.	Rubin DB, Thomas N. Matching using estimated propensity scores: relating theory to practice. Biometrics, 1996: 249-264.
18.	Heckman JJ. The scientific model of causality. Sociol Method, 2005, 35(1): 1-97.
19.	Fang G, Brooks JM, Chrischilles EA. Apples and oranges. Interpretations of risk adjustment and instrumental variable estimates of intended treatment effects using observational data. Am J Epidemiol, 2012, 175(1): 60-65.
20.	Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream. Epidemiology, 2006, 17(4): 360-372.
21.	Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Associat, 1996, 91(434): 444-455.
22.	Gail MH, Wieand S, Piantadosi S. Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika, 1984, 71(3): 431-444.
23.	Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol, 1987, 125(5): 761-768.
24.	Geng Z. Collapsibility of relative risk in contingency tables with a response variable. J Royal Stat Society, 1992, 54(2): 585-593.
25.	Harris KM, Remler DK. Who is the marginal patient? Understanding instrumental variables estimates of treatment effects. Health Serv Res, 1998, 33(5 Pt 1): 1337.
26.	Laborde-Castérot H, Agrinier N, Thilly N. Performing both propensity score and instrumental variable analyses in observational studies often leads to discrepant results: a systematic review. J Clin Epidemiol, 2015, 68(10): 1232-1240.

1. Greenland S, Pearl J, Robins JM. Confounding and collapsibility in causal inference. Stat Sci, 1999, 14(1): 29-46.
2. Ergina PL, Cook JA, Blazeby JM, et al. Challenges in evaluating surgical innovation. Lancet, 2009, 374(9695): 1097-1104.
3. Lonjon G, Porcher R, Ergina P, et al. Potential pitfalls of reporting and bias in observational studies with propensity score analysis assessing a surgical procedure: a methodological systematic review. Ann Surg, 2017, 265(5): 901-909.
4. Rosenbaum PR. Observational Studies. New York: Springer, 2002.
5. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika, 1983, 70(1): 41-55.
6. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology, 2000, 11(5): 550-560.
7. Sato T, Matsuyama Y. Marginal structural models as a tool for standardization. Epidemiology, 2003, 14(6): 680-686.
8. Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Stat Med, 2014, 33(13): 2297-2340.
9. Imbens GW, Rubin DB. Causal inference in statistics, social, and biomedical sciences. Cambridge: Cambridge University Press, 2015.
10. 郭申阳. 倾向值分析: 统计方法与应用. 重庆: 重庆大学出版社, 2012.
11. Greenland S. Randomization, statistics, and causal inference. Epidemiology, 1990, 1(6): 421-429.
12. Greenland S, Robins JM. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol, 1986, 15(3): 413-419.
13. Austin PC, Grootendorst P, Normand SL, et al. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Stat Med, 2007, 26(4): 754-768.
14. Shah BR, Laupacis A, Hux JE, et al. Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. J Clin Epidemiol, 2005, 58(6): 550-559.
15. Austin PC. A tutorial and case study in propensity score analysis: an application to estimating the effect of in-hospital smoking cessation counseling on mortality. Multivariate Behav Res, 2011, 46(1): 119-151.
16. Robins JM. Marginal structural models versus structural nested models as tools for causal inference. New York: Springer, 2000: 95-133.
17. Rubin DB, Thomas N. Matching using estimated propensity scores: relating theory to practice. Biometrics, 1996: 249-264.
18. Heckman JJ. The scientific model of causality. Sociol Method, 2005, 35(1): 1-97.
19. Fang G, Brooks JM, Chrischilles EA. Apples and oranges. Interpretations of risk adjustment and instrumental variable estimates of intended treatment effects using observational data. Am J Epidemiol, 2012, 175(1): 60-65.
20. Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream. Epidemiology, 2006, 17(4): 360-372.
21. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Associat, 1996, 91(434): 444-455.
22. Gail MH, Wieand S, Piantadosi S. Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika, 1984, 71(3): 431-444.
23. Greenland S. Interpretation and choice of effect measures in epidemiologic analyses. Am J Epidemiol, 1987, 125(5): 761-768.
24. Geng Z. Collapsibility of relative risk in contingency tables with a response variable. J Royal Stat Society, 1992, 54(2): 585-593.
25. Harris KM, Remler DK. Who is the marginal patient? Understanding instrumental variables estimates of treatment effects. Health Serv Res, 1998, 33(5 Pt 1): 1337.
26. Laborde-Castérot H, Agrinier N, Thilly N. Performing both propensity score and instrumental variable analyses in observational studies often leads to discrepant results: a systematic review. J Clin Epidemiol, 2015, 68(10): 1232-1240.

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