Assessing Sensitivity to Unmeasured Confounding Using a Simulated Potential Confounder.
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| Title: | Assessing Sensitivity to Unmeasured Confounding Using a Simulated Potential Confounder. |
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| Authors: | Carnegie, Nicole Bohme1 (AUTHOR) carnegin@uwm.edu, Harada, Masataka2 (AUTHOR), Hill, Jennifer L.3 (AUTHOR) |
| Source: | Journal of Research on Educational Effectiveness. Jul-Sep2016, Vol. 9 Issue 3, p395-420. 26p. |
| Subject Terms: | *Computer software, Sensitivity analysis, Mathematical variables, Simulation methods & models, Numerical analysis, Parameter estimation |
| Abstract: | A major obstacle to developing evidenced-based policy is the difficulty of implementing randomized experiments to answer all causal questions of interest. When using a nonexperimental study, it is critical to assess how much the results could be affected by unmeasured confounding. We present a set of graphical and numeric tools to explore the sensitivity of causal estimates to the presence of an unmeasured confounder. We characterize the confounder through two parameters that describe the relationships between (a) the confounder and the treatment assignment and (b) the confounder and the outcome variable. Our approach has two primary advantages over similar approaches that are currently implemented in standard software. First, it can be applied to both continuous and binary treatment variables. Second, our method for binary treatment variables allows the researcher to specify three possible estimands (average treatment effect, effect of the treatment on the treated, effect of the treatment on the controls). These options are all implemented in an R package called treatSens. We demonstrate the efficacy of the method through simulations. We illustrate its potential usefulness in practice in the context of two policy applications. [ABSTRACT FROM PUBLISHER] |
| Copyright of Journal of Research on Educational Effectiveness is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Abstract: | A major obstacle to developing evidenced-based policy is the difficulty of implementing randomized experiments to answer all causal questions of interest. When using a nonexperimental study, it is critical to assess how much the results could be affected by unmeasured confounding. We present a set of graphical and numeric tools to explore the sensitivity of causal estimates to the presence of an unmeasured confounder. We characterize the confounder through two parameters that describe the relationships between (a) the confounder and the treatment assignment and (b) the confounder and the outcome variable. Our approach has two primary advantages over similar approaches that are currently implemented in standard software. First, it can be applied to both continuous and binary treatment variables. Second, our method for binary treatment variables allows the researcher to specify three possible estimands (average treatment effect, effect of the treatment on the treated, effect of the treatment on the controls). These options are all implemented in an R package called treatSens. We demonstrate the efficacy of the method through simulations. We illustrate its potential usefulness in practice in the context of two policy applications. [ABSTRACT FROM PUBLISHER] |
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| ISSN: | 19345747 |
| DOI: | 10.1080/19345747.2015.1078862 |
