%0 Journal Article %1 Baiocchi2014 %A Baiocchi, Michael %A Cheng, Jing %A Small, Dylan S %D 2014 %J Statistics in medicine %K Causality ConfoundingFactors(Epidemiology) DataInterpretation Humans Infant Newborn ObservationalStudyasTopic ObservationalStudyasTopic:statistics&numeric OutcomeAssessment(HealthCare) OutcomeAssessment(HealthCare):statistics&num Statistical %N 13 %P 2297-340 %R 10.1002/sim.6128 %T Instrumental variable methods for causal inference. %U http://www.ncbi.nlm.nih.gov/pubmed/24599889 %V 33 %X A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead, an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration, which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies.

@article{Baiocchi2014, abstract = {A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead, an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration, which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Baiocchi, Michael and Cheng, Jing and Small, Dylan S}, biburl = {https://www.bibsonomy.org/bibtex/2c095a9216a33314e98421d053ae4c1ca/jepcastel}, doi = {10.1002/sim.6128}, interhash = {7264f52329dfbc07084fdbd09127a598}, intrahash = {c095a9216a33314e98421d053ae4c1ca}, issn = {1097-0258}, journal = {Statistics in medicine}, keywords = {Causality ConfoundingFactors(Epidemiology) DataInterpretation Humans Infant Newborn ObservationalStudyasTopic ObservationalStudyasTopic:statistics&numeric OutcomeAssessment(HealthCare) OutcomeAssessment(HealthCare):statistics&num Statistical}, month = {6}, note = {Variables instrumentals}, number = 13, pages = {2297-340}, pmid = {24599889}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Instrumental variable methods for causal inference.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24599889}, volume = 33, year = 2014 }