Instruments for causal inference: an epidemiologist's dream?
The use of instrumental variable (IV) methods is attractive because, even in the presence of unmeasured confounding, such methods may consistently estimate the average causal effect of an exposure on an outcome. However, for this consistent estimation to be achieved, several strong conditions must hold. We review the definition of an instrumental variable, describe the conditions required to obtain consistent estimates of causal effects, and explore their implications in the context of a recent application of the instrumental variables approach. We also present (1) a description of the connection between 4 causal models-counterfactuals, causal directed acyclic graphs, nonparametric structural equation models, and linear structural equation models-that have been used to describe instrumental variables methods; (2) a unified presentation of IV methods for the average causal effect in the study population through structural mean models; and (3) a discussion and new extensions of instrumental variables methods based on assumptions of monotonicity.
Hernán
Miguel A
MA
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA. Miguel_herman@post.harvard.edu
Robins
James M
JM
D015982:Bias; D015986:Confounding Factors, Epidemiologic; D004311:Double-Blind Method; D016021:Epidemiologic Studies; D015233:Models, Statistical; D010818:Practice Patterns, Physicians'; D011336:Probability; D016032:Randomized Controlled Trials as Topic; D018709:Statistics, Nonparametric