Bias Amplification and Bias Unmasking
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| Title: | Bias Amplification and Bias Unmasking |
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| Language: | English |
| Authors: | Middleton, Joel A., Scott, Marc A., Diakow, Ronli, Hill, Jennifer L. |
| Source: | Grantee Submission. 2016 24. |
| Peer Reviewed: | Y |
| Page Count: | 17 |
| Publication Date: | 2016 |
| Sponsoring Agency: | Institute of Education Sciences (ED) |
| Contract Number: | R305D110037 R305B120017 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Statistical Bias, Identification, Evaluation Methods, Measurement Techniques, Statistical Analysis, Predictor Variables, Case Studies, Models |
| DOI: | 10.1093/pan/mpw015 |
| Abstract: | In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due to "excluding" observed covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of bias unmasking and shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias "unmasking" with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications. |
| Abstractor: | As Provided |
| Number of References: | 43 |
| IES Funded: | Yes |
| Entry Date: | 2018 |
| Accession Number: | ED590328 |
| Database: | ERIC |
| Abstract: | In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due to "excluding" observed covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of bias unmasking and shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias "unmasking" with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications. |
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| DOI: | 10.1093/pan/mpw015 |
