Article

Instruments for Causal Inference: An Epidemiologist's Dream?

Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Epidemiology (Impact Factor: 6.18). 08/2006; 17(4):360-72. DOI: 10.1097/01.ede.0000222409.00878.37
Source: PubMed

ABSTRACT 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.

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    • "The use of genetic variants as instrumental variables in observational data has been termed 'Mendelian randomization' [6] [7]. Many reviews exist on the use of instrumental variables, in particular on the assumptions necessary to be an instrumental variable [8] [9], assessing the validity of instrumental variables [10], assumptions necessary for estimation of a causal effect parameter using instrumental variables [11] [12] [13], methods for effect estimation with multiple instrumental variables [14], with binary outcomes [15] [16], methods for the estimation of odds ratios [17], and guidelines for the reporting of instrumental variable analysis [18]. We seek to complement this literature by contributing a review to compare methods for instrumental variable analysis, with accompanying practical guidelines on their use. "
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    ABSTRACT: Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome. © The Author(s) 2015.
    Statistical Methods in Medical Research 07/2015; DOI:10.1177/0962280215597579 · 2.96 Impact Factor
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    • "Intuitively, the IV method seeks to extract variation in treatment that is free of unmeasured confounders and uses this variation to estimate the treatment effect. For more information on IV methods, see Angrist et al. (1996), Newhouse and McClellan (1998), Greenland (2000), Hernán and Robins (2006), Cheng et al. (2009a), Baiocchi et al. (2014) and Imbens (2014). This paper is motivated by provider preference IV (PP IV) which is commonly used as an IV in health studies . "
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    ABSTRACT: Instrumental variable (IV) methods are widely used to adjust for the bias in estimating treatment effects caused by unmeasured confounders in observational studies. In this manuscript, we provide empirical and theoretical evidence that the IV methods may result in biased treatment effects if applied on a data set in which subjects are preselected based on their received treatments. We frame this as a selection bias problem and propose a procedure that identifies the treatment effect of interest as a function of a vector of sensitivity parameters. We also list assumptions under which analyzing the preselected data does not lead to a biased treatment effect estimate. The performance of the proposed method is examined using simulation studies. We applied our method on The Health Improvement Network (THIN) database to estimate the comparative effect of metformin and sulfonylureas on weight gain among diabetic patients.
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    • "This is done out of necessity, for example, we cannot measure the actual preference of the physician when using a preference-based instrument, or we sometimes only have the means to measure approximate locations in the genome when using a genetic-based instrument. Although the use of such a noncausal instrument could satisfy the other identifying assumptions, this measurement error complicates our interpretation of a LATE-like effect (Hernán and Robins, 2006). "
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    ABSTRACT: Discussion of "Instrumental Variables: An Econometrician's Perspective" by Guido W. Imbens [arXiv:1410.0163].
    Statistical Science 10/2014; 29(3). DOI:10.1214/14-STS491 · 1.69 Impact Factor
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