Understanding the Assumptions Underlying Instrumental Variable Analyses: a Brief Review of Falsification Strategies and Related Tools

[1]  M. Hernán,et al.  The challenging interpretation of instrumental variable estimates under monotonicity. , 2018, International journal of epidemiology.

[2]  C. A. Rietveld,et al.  Pleiotropy-robust Mendelian Randomization , 2016, bioRxiv.

[3]  James M. Robins,et al.  Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes , 2018, Journal of the American Statistical Association.

[4]  Eric Tchetgen Tchetgen,et al.  Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables , 2016, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[5]  Dylan S. Small,et al.  Instrumental Variable Estimation with a Stochastic Monotonicity Assumption , 2017 .

[6]  S. Swanson.  Instrumental Variable Analyses in Pharmacoepidemiology: What Target Trials Do We Emulate? , 2017, Current Epidemiology Reports.

[7]  Eric J. Tchetgen Tchetgen,et al.  The GENIUS Approach to Robust Mendelian Randomization Inference , 2017, bioRxiv.

[8]  M. Hernán,et al.  Nature as a Trialist?: Deconstructing the Analogy Between Mendelian Randomization and Randomized Trials. , 2017, Epidemiology.

[9]  Catherine R. Lesko,et al.  Instrumental Variable Analyses and Selection Bias , 2017, Epidemiology.

[10]  S. Swanson.  Commentary: Can We See the Forest for the IVs?: Mendelian Randomization Studies with Multiple Genetic Variants. , 2016, Epidemiology.

[11]  J. Robins,et al.  On falsification of the binary instrumental variable model , 2016, Biometrika.

[12]  Dylan S. Small,et al.  A review of instrumental variable estimators for Mendelian randomization , 2015, Statistical methods in medical research.

[13]  Neil M Davies,et al.  How to compare instrumental variable and conventional regression analyses using negative controls and bias plots , 2016, Journal of Epidemiology & Community Health.

[14]  C. A. Rietveld,et al.  Pleiotropy-robust Mendelian Randomization , 2016, bioRxiv.

[15]  G. Davey Smith,et al.  Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator , 2016, Genetic epidemiology.

[16]  Dylan S. Small,et al.  A Sensitivity Analysis to Assess Bias Due to Selecting Subjects Based on Treatment Received. , 2015, Epidemiology.

[17]  Miguel A. Hernán,et al.  Bounding the per-protocol effect in randomized trials: an application to colorectal cancer screening , 2015, Trials.

[18]  Tyler J. VanderWeele,et al.  Is the Risk Difference Really a More Heterogeneous Measure? , 2015, Epidemiology.

[19]  John W Jackson,et al.  Toward a clearer portrayal of confounding bias in instrumental variable applications. , 2015, Epidemiology.

[20]  James M. Robins,et al.  Definition and Evaluation of the Monotonicity Condition for Preference-based Instruments , 2015, Epidemiology.

[21]  Saskia le Cessie,et al.  Mendelian randomization studies: a review of the approaches used and the quality of reporting. , 2015, International journal of epidemiology.

[22]  G. Davey Smith,et al.  Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression , 2015, International journal of epidemiology.

[23]  A. G. Boef,et al.  Mendelian randomization studies in the elderly. , 2015, Epidemiology.

[24]  James M Robins,et al.  Selecting on treatment: a pervasive form of bias in instrumental variable analyses. , 2015, American journal of epidemiology.

[25]  Eric J Tchetgen Tchetgen,et al.  Methodological Challenges in Mendelian Randomization , 2014, Epidemiology.

[26]  Dylan S. Small,et al.  The causal effect of malaria on stunting: a Mendelian randomization and matching approach. , 2013, International journal of epidemiology.

[27]  Miguel A Hernán,et al.  Commentary: how to report instrumental variable analyses (suggestions welcome). , 2013, Epidemiology.

[28]  Neil M Davies,et al.  Issues in the reporting and conduct of instrumental variable studies: a systematic review. , 2013, Epidemiology.

[29]  J. Robins,et al.  American Journal of Epidemiology Practice of Epidemiology Credible Mendelian Randomization Studies: Approaches for Evaluating the Instrumental Variable Assumptions , 2022 .

[30]  Roland R. Ramsahai,et al.  Likelihood analysis of the binary instrumental variable model , 2011 .

[31]  S. Thompson,et al.  Avoiding bias from weak instruments in Mendelian randomization studies. , 2011, International journal of epidemiology.

[32]  J. Pearl,et al.  Causal inference , 2011, Twenty-one Mental Models That Can Change Policing.

[33]  John C. Ham,et al.  The Hausman Test and Weak Instruments , 2011 .

[34]  Angus Deaton.  Instruments, Randomization, and Learning about Development , 2010 .

[35]  M. Lipsitch,et al.  Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies , 2010, Epidemiology.

[36]  Judea Pearl,et al.  Causal Inference , 2010 .

[37]  James M. Robins,et al.  Analysis of the Binary Instrumental Variable Model , 2010 .

[38]  M Alan Brookhart,et al.  American Journal of Epidemiology Practice of Epidemiology Instrumental Variable Analysis for Estimation of Treatment Effects with Dichotomous Outcomes , 2022 .

[39]  Joshua D. Angrist,et al.  Mostly Harmless Econometrics: An Empiricist's Companion , 2008 .

[40]  M. Hernán,et al.  Does obesity shorten life? The importance of well-defined interventions to answer causal questions , 2008, International Journal of Obesity.

[41]  J. Robins,et al.  Instruments for Causal Inference: An Epidemiologist's Dream? , 2006, Epidemiology.

[42]  Christopher R. Taber,et al.  An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling , 2002, The Journal of Human Resources.

[43]  Alberto Abadie.  Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models , 2002 .

[44]  G. Davey Smith,et al.  Epidemiology--is it time to call it a day? , 2001, International journal of epidemiology.

[45]  S. Greenland.  An introduction To instrumental variables for epidemiologists , 2000, International journal of epidemiology.

[46]  J. Pearl,et al.  Bounds on Treatment Effects from Studies with Imperfect Compliance , 1997 .

[47]  J. Angrist,et al.  Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity , 1995 .

[48]  Glenn D. Rudebusch,et al.  Judging Instrument Relevance in Instrumental Variables Estimation , 1996 .

[49]  S G Baker,et al.  The paired availability design: a proposal for evaluating epidural analgesia during labor. , 1994, Statistics in medicine.

[50]  Joshua D. Angrist,et al.  Identification of Causal Effects Using Instrumental Variables , 1993 .

[51]  J. Angrist,et al.  Identification and Estimation of Local Average Treatment Effects , 1994 .

[52]  C. Manski.  Nonparametric Bounds on Treatment Effects , 1989 .

[53]  Richard Startz,et al.  The Distribution of the Instrumental Variables Estimator and its T-Ratiowhen the Instrument is a Poor One , 1988 .

[54]  J. Hausman.  Specification tests in econometrics , 1978 .