Estimating causal effects

International Journal of Epidemiology

Volumn 31, Issue 2, 2002, Pages 422-429

  Maldonado, George ^a   Greenland, Sander ^b  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EPIDEMIOLOGY; METHODOLOGY;

ANALYTIC METHOD; ARTICLE; CORRELATION COEFFICIENT; EPIDEMIOLOGICAL DATA; HUMAN; MEASUREMENT; MEDICAL RESEARCH; META ANALYSIS; METHODOLOGY; POPULATION; PRIORITY JOURNAL; REGRESSION ANALYSIS; STATISTICAL ANALYSIS;

EID: 0036100380     PISSN: 03005771     EISSN: None     Source Type: Journal    
DOI: 10.1093/intjepid/31.2.422     Document Type: Article

References (42)

1. Chapter 4: Measures of effect and measures of association
2. Causation
3. Estimating causal effects of treatments in randomized and nonrandomized studies
4. Identifiability, exchangeability, and epidemiological confounding
5. Interpretation and choice of effect measures in epidemiologic analyses
6. Confounding and collapsibility in causal inference
7. (1923) Sur les applications de la thar des probabilities aux experiences Agaricales: Essay des principle
8. Randomization models for matched and unmatched 2 × 2 tables
9. Bayesian inference for causal effects: The role of randomization
10. Causal inference without counterfactuals (with discussion)
11. Causal inference in the social and behavioral sciences
12. Comment: Neyman (1923) and causal inference in experiments and observational studies
13. Cause and counterfactual
14. A theory of conditionals
15. Causal analysis in the health sciences
16. Causal diagrams for epidemiologic research
17. Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism
18. Standardized regression coefficients: A further critique and a review of alternatives
19. Absence of confounding does not correspond to collapsibility of the rate ratio or rate difference
20. The causal-contrast study design
21. Observational Studies
22. Causal inference from complex longitudinal data
23. Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models
24. Confounding in health research
25. Matching and unrelatedness
26. Causal diagrams for empirical research
27. Falsification, verification and causal inference in epidemiology
28. A critical look at some popular meta-analytic methods
29. Random effects meta-analyses are not always conservative
30. Basic methods for sensitivity analysis and external adjustment
31. Informal evaluation of bias may be inadequate
32. Sensitivity analyses would help
33. The confounding product