Balancing Weights for Causal Inference

In this chapter, the authors introduce the balancing approach to weighting for covariate balance and causal inference. They begin by providing a framework for causal inference in observational studies, including typical assumptions necessary for the identification of average treatment effects. The authors motivate the task of finding weights that balance covariates and unify a variety of methods from the literature. It discusses several implementation and design choices for finding balancing weights in practice and discuss the trade-offs of these choices using an example from the canonical LaLonde data. An alternative approach for IPW weights instead directly targets the balancing property by seeking weights that balance covariates in the sample at hand. Arguably, the most important design choice in the balancing approach is the choice of the model class M over which imbalance is minimized. A common approach to estimating treatment effects in observational studies involves least squares linear regression.