The role of covariate balance in observational studies |
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| Authors: | Jason J Sauppe Sheldon H Jacobson |
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| Affiliation: | 1. Department of Computer Science, University of Wisconsin–La Crosse, La Crosse, WI 54601, USA;2. Department of Computer Science, University of Illinois at Urbana‐Champaign, Urbana, IL 61801, USA |
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| Abstract: | Observational data are prevalent in many fields of research, and it is desirable to use this data to make causal inferences. Because this data is nonrandom, additional assumptions are needed in order to construct unbiased estimators for causal effects. The standard assumption is strong ignorability, though it is often impossible to achieve the level of covariate balance that it requires. As such, researchers often settle for lesser balance levels within their datasets. However, these balance levels are generally insufficient to guarantee an unbiased estimate of the treatment effect without further assumptions. This article presents several extensions to the strong ignorability assumption that address this issue. Under these additional assumptions, specific levels of covariate balance are both necessary and sufficient for the treatment effect estimate to be unbiased. There is a trade‐off, however: as balance decreases, stronger assumptions are required to guarantee estimator unbiasedness. These results unify parametric and nonparametric adjustment methods for causal inference and are actualized by the Balance Optimization Subset Selection framework, which identifies the best level of balance that can be achieved within a dataset. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 323–344, 2017 |
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| Keywords: | observational data causal inference matching balance optimization strong ignorability |
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