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1. Full Text Instrumental variables II: instrumental variable application—in 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalance
by Rassen, Jeremy A and Brookhart, M. Alan and Glynn, Robert J and Mittleman, Murray A and Schneeweiss, Sebastian
Journal of Clinical Epidemiology, ISSN 0895-4356, 2009, Volume 62, Issue 12, pp. 1233 - 1241
Abstract Objective An instrumental variable (IV) is an unconfounded proxy for a study exposure that can be used to estimate a causal effect in the presence of... 
Internal Medicine | Pharmacoepidemiology | Instrumental variable | Confounding factor (epidemiology) | Partial R-squared | Physician prescribing preference | Antipsychotic agents | Mahalanobis distance | PROPENSITY SCORE | DETERMINANTS | ADMINISTRATIVE DATA | RISK | CLINICAL COMORBIDITY INDEX | CLAIMS DATA | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | WEAK INSTRUMENTS | EPIDEMIOLOGISTS | PATIENT | INHIBITORS | Antipsychotic Agents - administration & dosage | Epidemiologic Research Design | Practice Patterns, Physicians' - statistics & numerical data | Algorithms | Confounding Factors (Epidemiology) | Antipsychotic Agents - adverse effects | Humans | Mortality | Aged, 80 and over | Aged | Pennsylvania - epidemiology | British Columbia - epidemiology | Medical colleges | Public health | Prescription writing
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2. Instrumental variables
by Bowden, Roger J. 1943- (Roger John) and Turkington, Darrell A
1984, Econometric Society monographs in quantitative economics, ISBN 9780521262415, Volume no. 6, viii, 227
Recent advances in establishing the nature and scope of estimators in econometrics have shed more light on the importance of instrumental variables. In this... 
Instrumental variables (Statistics)
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3. Full Text Instrumental Variables: Application and Limitations
by Edwin P. Martens and Wiebe R. Pestman and Anthonius de Boer and Svetlana V. Belitser and Olaf H. Klungel
Epidemiology, ISSN 1044-3983, 5/2006, Volume 17, Issue 3, pp. 260 - 267
To correct for confounding, the method of instrumental variables (IV) has been proposed. Its use in medical literature is still rather limited because of... 
Estimation bias | Encouragement | Least squares | Instrumental variables | Instrumental variables estimation | Sampling bias | Unbiased estimators | Cigarette smoking | Epidemiology | Estimators | WEAK | TRIALS | REDUCE MORTALITY | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | MODELS | ESTIMATORS | ACUTE MYOCARDIAL-INFARCTION | SMOKING | MENDELIAN RANDOMIZATION | CANCER | LEAST-SQUARES | Epidemiologic Studies | Confounding Factors (Epidemiology) | Models, Statistical | United States | Bias
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4. Full Text Errors-in-variables identification in dynamic networks — Consistency results for an instrumental variable approach
by Dankers, Arne and Van den Hof, Paul M.J and Bombois, Xavier and Heuberger, Peter S.C
Automatica, ISSN 0005-1098, 12/2015, Volume 62, pp. 39 - 50
In this paper we consider the identification of a linear module that is embedded in a dynamic network using noisy measurements of the internal variables of the... 
Instrumental variables | Dynamic networks | System identification | Closed-loop system identification | Errors-in-variables | MODELS | FEEDBACK | AUTOMATION & CONTROL SYSTEMS | SYSTEM-IDENTIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | Analysis | Sensors | Engineering Sciences | Automatic
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5. Full Text Model-Implied Instrumental Variable—Generalized Method of Moments (MIIV-GMM) Estimators for Latent Variable Models
by Bollen, Kenneth A and Kolenikov, Stanislav and Bauldry, Shawn
Psychometrika, ISSN 0033-3123, 1/2014, Volume 79, Issue 1, pp. 20 - 50
The common maximum likelihood (ML) estimator for structural equation models (SEMs) has optimal asymptotic properties under ideal conditions (e.g., correct... 
factor analysis | instrumental variables | latent variables | Psychology | Assessment, Testing and Evaluation | Psychometrics | Statistical Theory and Methods | structural equation models | generalized method of moments | Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law | LEAST-SQUARES 2SLS | REGRESSION | CONFIRMATORY FACTOR-ANALYSIS | MISSPECIFICATION | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | WEAK INSTRUMENTS | TEST STATISTICS | SOCIAL SCIENCES, MATHEMATICAL METHODS | PSYCHOLOGY, MATHEMATICAL | ERROR | Humans | Psychometrics - methods | Statistics as Topic - methods | Data processing
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6. Full Text TWO-SAMPLE INSTRUMENTAL VARIABLES ESTIMATORS
by Atsushi Inoue and Gary Solon
The Review of Economics and Statistics, ISSN 0034-6535, 8/2010, Volume 92, Issue 3, pp. 557 - 561
Following an influential article by Angrist and Krueger (1992) on two-sample instrumental variables (TSIV) estimation, numerous empirical researchers have... 
Maximum likelihood estimation | Analytical estimating | Least squares | Instrumental variables estimation | Instrumental variables | Standard error | Covariance matrices | Estimators | Consistent estimators | Estimation methods | Articles | EDUCATIONAL-ATTAINMENT | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | Usage | Instrumental variables (Statistics) | Analysis | Estimation theory
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7. Full Text The medical care costs of obesity: An instrumental variables approach
by Cawley, John and Meyerhoefer, Chad
Journal of Health Economics, ISSN 0167-6296, 01/2012, Volume 31, Issue 1, pp. 219 - 230
This paper is the first to use the method of instrumental variables (IV) to estimate the impact of obesity on medical costs in order to address the endogeneity... 
Externalities | Obesity | Medical care costs | Insurance | UNITED-STATES | ENVIRONMENT | US ADULTS | GENERALIZED-METHOD | HEALTH-INSURANCE | OVERWEIGHT | GENES | HEALTH CARE SCIENCES & SERVICES | BODY-MASS INDEX | HEALTH POLICY & SERVICES | ECONOMICS | WEIGHT | CHILDHOOD OBESITY | Young Adult | Models, Econometric | Health Care Costs - statistics & numerical data | Humans | Middle Aged | Adult | Female | Male | Obesity - economics | Medical errors | Analysis | Health care industry | Medical economics | Medical care, Cost of | Externalities (Economics) | Studies | Economic models | Health care expenditures | Externality | Impact analysis | Instrumental variables
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8. Full Text On the use of instrumental variables in accounting research
by Larcker, David F and Rusticus, Tjomme O
Journal of Accounting and Economics, ISSN 0165-4101, 2010, Volume 49, Issue 3, pp. 186 - 205
Instrumental variable (IV) methods are commonly used in accounting research (e.g., earnings management, corporate governance, executive compensation, and... 
Instrumental variables | Endogeneity | Disclosure | Cost of capital | INFORMATION ASYMMETRY | STOCK-OPTIONS | DISCLOSURE LEVEL | EARNINGS MANAGEMENT | CONFERENCE CALLS | BUSINESS, FINANCE | WEAK INSTRUMENTS | MARKET VALUATION | ECONOMIC CONSEQUENCES | VALUE-RELEVANCE | ECONOMICS | EXECUTIVE-COMPENSATION | Endogeneity Instrumental variables Disclosure Cost of capital | Corporate governance | Accounting | Financial disclosure | Studies | Estimating techniques | Econometrics
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9. Full Text Instrumental Variables Estimation in Political Science: A Readers' Guide
by Allison J. Sovey and Donald P. Green
American Journal of Political Science, ISSN 0092-5853, 1/2011, Volume 55, Issue 1, pp. 188 - 200
The use of instrumental variables regression in political science has evolved from an obscure technique to a staple of the political science tool kit. Yet the... 
Natural experiments | AJPS WORKSHOP | Control groups | Rain | Political science | Encouragement | Instrumental variables | Instrumental variables estimation | Research design | Political campaigns | Estimators | ECONOMETRICS | WEAK INSTRUMENTS | VOTER TURNOUT | POLITICAL SCIENCE | NATURAL EXPERIMENTS | INFERENCE | RANDOMIZED EXPERIMENTS | Regression analysis | Estimating techniques | Journals
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10. Full Text Instrumental variable methods for causal inference
by Baiocchi, Michael and Cheng, Jing and Small, Dylan S
Statistics in Medicine, ISSN 0277-6715, 06/2014, Volume 33, Issue 13, pp. 2297 - 2340
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... 
comparative effectiveness | confounding | instrumental variables | observational study | Confounding | Comparative effectiveness | Observational study | Instrumental variables | UNITED-STATES | MEDICINE, RESEARCH & EXPERIMENTAL | RESPONSE MODELS | PROPENSITY SCORE | MEDICAL INFORMATICS | PREMATURE-INFANTS | BIRTH-WEIGHT | STATISTICS & PROBABILITY | MENDELIAN RANDOMIZATION | RANDOMIZED CLINICAL-TRIALS | NONCOMPLIANCE | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | IMPERFECT COMPLIANCE | TO-TREAT | MATHEMATICAL & COMPUTATIONAL BIOLOGY | Data Interpretation, Statistical | Confounding Factors (Epidemiology) | Outcome Assessment (Health Care) - statistics & numerical data | Humans | Observational Studies as Topic - statistics & numerical data | Causality | Infant, Newborn
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11. Full Text Use of allele scores as instrumental variables for Mendelian randomization
by Burgess, Stephen and Thompson, Simon G
International Journal of Epidemiology, ISSN 0300-5771, 08/2013, Volume 42, Issue 4, pp. 1134 - 1144
Background An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of... 
Genetic risk scores | Weak instruments | Allele scores | Instrumental variables | Mendelian randomization | PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH | genetic risk scores | instrumental variables | BIAS | allele scores | weak instruments | Mendelian Randomization Analysis - methods | Bias | Humans | Risk Factors | Alleles | Models, Genetic | Causality | Methodology
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12. Full Text Genetic markers as instrumental variables
by von Hinke, Stephanie and Davey Smith, George and Lawlor, Debbie A and Propper, Carol and Windmeijer, Frank
Journal of Health Economics, ISSN 0167-6296, 01/2016, Volume 45, pp. 131 - 148
The use of genetic markers as instrumental variables (IV) is receiving increasing attention from economists, statisticians, epidemiologists and social... 
ALSPAC | Instrumental variables | Mendelian randomization | Genetic variants | Potential outcomes | FTO GENE | VARIANTS | CAUSAL INFERENCE | PRINCIPAL STRATIFICATION |