scholarly journals On solving endogeneity with invalid instruments: an application to investment equations

2017 ◽  
Vol 181 (3) ◽  
pp. 689-716 ◽  
Author(s):  
Antonio F. Galvao ◽  
Gabriel Montes–Rojas ◽  
Jose Olmo ◽  
Suyong Song
Keyword(s):  
Invalid Instruments

scholarly journals Identification and Inference With Many Invalid Instruments

2015 ◽  
Vol 33 (4) ◽  
pp. 474-484 ◽  
Author(s):  
Michal Kolesár ◽  
Raj Chetty ◽  
John Friedman ◽  
Edward Glaeser ◽  
Guido W. Imbens
Keyword(s):  
Invalid Instruments

scholarly journals Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

2016 ◽  
Vol 40 (4) ◽  
pp. 304-314 ◽  
Author(s):  
Jack Bowden ◽  
George Davey Smith ◽  
Philip C. Haycock ◽  
Stephen Burgess
Keyword(s):  
Mendelian Randomization ◽  
Consistent Estimation ◽  
Invalid Instruments ◽  
Weighted Median

scholarly journals Avoiding Invalid Instruments and Coping with Weak Instruments

2006 ◽  
Vol 20 (4) ◽  
pp. 111-132 ◽  
Author(s):  
Michael P Murray
Keyword(s):  
Instrumental Variable ◽  
Ordinary Least Squares ◽  
Paper Briefly ◽  
Weak Instruments ◽  
Instrumental Variable Estimation ◽  
Invalid Instruments ◽  
Valid Instrument ◽  
Disturbance Term ◽  
And Coping ◽  
Instrumental Variable Estimator

Archimedes said, “Give me the place to stand, and a lever long enough, and I will move the Earth.” Economists have their own powerful lever: the instrumental variable estimator. The instrumental variable estimator can avoid the bias that ordinary least squares suffers when an explanatory variable in a regression is correlated with the regression's disturbance term. But, like Archimedes' lever, instrumental variable estimation requires both a valid instrument on which to stand and an instrument that isn't too short (or “too weak”). This paper briefly reviews instrumental variable estimation, discusses classic strategies for avoiding invalid instruments (instruments themselves correlated with the regression's disturbances), and describes recently developed strategies for coping with weak instruments (instruments only weakly correlated with the offending explanator).

scholarly journals Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome

2017 ◽  
Author(s):  
Gibran Hemani ◽  
Jack Bowden ◽  
Philip Haycock ◽  
Jie Zheng ◽  
Oliver Davis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
False Discovery Rates ◽  
Level Data ◽  
Invalid Instruments ◽  
Major Application

AbstractA major application for genome-wide association studies (GWAS) has been the emerging field of causal inference using Mendelian randomization (MR), where the causal effect between a pair of traits can be estimated using only summary level data. MR depends on SNPs exhibiting vertical pleiotropy, where the SNP influences an outcome phenotype only through an exposure phenotype. Issues arise when this assumption is violated due to SNPs exhibiting horizontal pleiotropy. We demonstrate that across a range of pleiotropy models, instrument selection will be increasingly liable to selecting invalid instruments as GWAS sample sizes continue to grow. Methods have been developed in an attempt to protect MR from different patterns of horizontal pleiotropy, and here we have designed a mixture-of-experts machine learning framework (MR-MoE 1.0) that predicts the most appropriate model to use for any specific causal analysis, improving on both power and false discovery rates. Using the approach, we systematically estimated the causal effects amongst 2407 phenotypes. Almost 90% of causal estimates indicated some level of horizontal pleiotropy. The causal estimates are organised into a publicly available graph database (http://eve.mrbase.org), and we use it here to highlight the numerous challenges that remain in automated causal inference.

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