GENIUS-MAWII: for robust Mendelian randomization with many weak invalid instruments.

Ting Ye, Zhonghua Liu, Baoluo Sun, Eric Tchetgen Tchetgen
Author Information
  1. Ting Ye: Department of Biostatistics, University of Washington, Seattle, USA. ORCID
  2. Zhonghua Liu: Department of Biostatistics, Columbia University, New York City, USA. ORCID
  3. Baoluo Sun: Department of Statistics and Data Science, National University of Singapore, Singapore, Singapore. ORCID
  4. Eric Tchetgen Tchetgen: Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, USA.

Abstract

Mendelian randomization (MR) addresses causal questions using genetic variants as instrumental variables. We propose a new MR method, G-Estimation under No Interaction with Unmeasured Selection (GENIUS)-MAny Weak Invalid IV, which simultaneously addresses the 2 salient challenges in MR: many weak instruments and widespread horizontal pleiotropy. Similar to MR-GENIUS, we use heteroscedasticity of the exposure to identify the treatment effect. We derive influence functions of the treatment effect, and then we construct a continuous updating estimator and establish its asymptotic properties under a many weak invalid instruments asymptotic regime by developing novel semiparametric theory. We also provide a measure of weak identification, an overidentification test, and a graphical diagnostic tool.

Keywords

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Word Cloud

Created with Highcharts 10.0.0weakmanyinstrumentsMendelianrandomizationMRaddressescausalinstrumentalvariablespleiotropytreatmenteffectasymptoticinvalidquestionsusinggeneticvariantsproposenewmethodG-EstimationInteractionUnmeasuredSelectionGENIUS-MAnyWeakInvalidIVsimultaneously2salientchallengesMR:widespreadhorizontalSimilarMR-GENIUSuseheteroscedasticityexposureidentifyderiveinfluencefunctionsconstructcontinuousupdatingestimatorestablishpropertiesregimedevelopingnovelsemiparametrictheoryalsoprovidemeasureidentificationoveridentificationtestgraphicaldiagnostictoolGENIUS-MAWII:robustinferenceexclusionrestrictionheteroscedasticerrorsmoments

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