Model and variable selection for causal inference

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To unbiasedly estimate a causal effect utilizing observational data, one must adjust for potentially confounding covariates. Identifying confounding variables using only substantive prior knowledge can be a very difficult task, especially in new areas of research or those where current subject-matter knowledge is sparse. An intuitive strategy to circumvent this problem is to adjust for all potential confounders in a statistical model. This strategy can, however, produce causal exposure effect estimators whose variance is unnecessarily large. Instead, model selection can be attempted. Recent simulation results have shown that classical statistical approaches for model selection often fail to produce unbiased causal effect estimators. Hence, there is a need for the creation of new model selection approaches specifically tailored for causal inference purposes.