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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 [ABSTRACT FROM AUTHOR]

Matching is widely used in the estimation of treatment effects in observational studies. However, the matching paradigm may be too restrictive in many cases because exact matches often do not exist in the available data. One mechanism for overcoming this issue is to relax the requirement of exact matching on some or all of the covariates (attributes that may affect the response to treatment) to a requirement of balance on the covariate distributions for the treatment and control groups. The balance optimization subset selection (BOSS) model can be used to identify a control group featuring optimal covariate balance. This paper explores the relationship between the matching and BOSS models and shows how BOSS subsumes matching. Complexity and approximation results are presented for the resulting models. Computational results demonstrate some of the important trade-offs between matching and BOSS. [ABSTRACT FROM AUTHOR]

To make causal inferences from observational data, researchers have often turned to matching methods. These methods are variably successful. We address issues with matching methods by redefining the matching problem as a subset selection problem. Given a set of covariates, we seek to find two subsets, a control group and a treatment group, so that we obtain optimal balance, or, in other words, the minimum discrepancy between the distributions of these covariates in the control and treatment groups. Our formulation captures the key elements of the Rubin causal model and translates nicely into a discrete optimization framework. [ABSTRACT FROM AUTHOR]