Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference*

By Michael T. M. Baiocchi, Jing Cheng. Statistics in Medicine, 2014

726 citations recorded by the source corpus.

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. Copyright © 2014 John Wiley & Sons, Ltd.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d94919c7f7a77419f35bf1bc0eb152aac785482a

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Citations in the corpus

Two-Sample Instrumental Variable Analyses Using Heterogeneous Samples (2017, methodology): “The most influential approach is the LATE framework (Imbens and Angrist, 1994, Baker and Lindeman, 1994, Angrist, Imbens and Rubin, 1996) that will be discussed in detail in Section 6.”
Confidence intervals for causal effects with invalid instruments by using two‐stage hard thresholding with voting (2016, neutral): “Often, covariates, potentially many, are controlled for to make instruments more plausibly valid (Hernán and Robins, 2006; Swanson and Hernán, 2013; Baiocchi et al., 2014; Imbens, 2014).”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “Readers who are unfamiliar with this language may find the tutorial by Baiocchi, Cheng and Small [3] helpful.”
Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, background): “Therefore, genotypes which should be unrelated to confounding factors can serve as instrumental variables (IVs) (Lawlor et al., 2008; Baiocchi et al., 2014), helping to eliminate the possibility of reverse causality.”
Latent class instrumental variables: A clinical and biostatistical perspective (2015, background): “Other names are ‘the IV estimand embedded in the Rubin Causal Model’ [9], ‘principal stratification approach to broken randomized experiments’ [10], ‘modern instrumental variables literature’ [11], ‘IV assumptions and estimation for binary IV and binary treatment’ [12], and ‘instrumental variable an”