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Constructing independent evidence from regression and instrumental variables with an application to the effect of violent conflict on altruism and risk preference

Constructing independent evidence from regression and instrumental variables with an application... To provide an unbiased estimate, a regression analysis depends, among other things, on there being no unmeasured confounding. Often, unmeasured confounding is thought to be possible, but not severe; leading to a secondary instrumental variables (IV) analysis. However, these two analyses are correlated. It is unclear how much independent evidence is provided by the IV analysis. We resolve this redundancy using a new estimator, which extracts the part of the regression estimator uncorrelated to the IV-based 2SLS estimator. We apply our approach to analyze the effect of exposure to violent conflict on preferences for altruistic behavior, time and risk. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Constructing independent evidence from regression and instrumental variables with an application to the effect of violent conflict on altruism and risk preference

Biostatistics & Epidemiology , Volume OnlineFirst: 21 – Aug 18, 2022

Constructing independent evidence from regression and instrumental variables with an application to the effect of violent conflict on altruism and risk preference

Abstract

To provide an unbiased estimate, a regression analysis depends, among other things, on there being no unmeasured confounding. Often, unmeasured confounding is thought to be possible, but not severe; leading to a secondary instrumental variables (IV) analysis. However, these two analyses are correlated. It is unclear how much independent evidence is provided by the IV analysis. We resolve this redundancy using a new estimator, which extracts the part of the regression estimator uncorrelated...
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Publisher
Taylor & Francis
Copyright
© 2022 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2022.2109910
Publisher site
See Article on Publisher Site

Abstract

To provide an unbiased estimate, a regression analysis depends, among other things, on there being no unmeasured confounding. Often, unmeasured confounding is thought to be possible, but not severe; leading to a secondary instrumental variables (IV) analysis. However, these two analyses are correlated. It is unclear how much independent evidence is provided by the IV analysis. We resolve this redundancy using a new estimator, which extracts the part of the regression estimator uncorrelated to the IV-based 2SLS estimator. We apply our approach to analyze the effect of exposure to violent conflict on preferences for altruistic behavior, time and risk.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Aug 18, 2022

Keywords: Independent pieces of evidence; instrumental variables; preferences; sensitivity analysis; unmeasured confounding

References