Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects

Understand

In the absence of unobserved confounders, matching and weighting methods are widely used to estimate causal quantities including the Average Treatment Effect on the Treated (ATT).

• Unfortunately, these methods do not necessarily achieve their goal of making the multivariate distribution of covariates for the control group identical to that of the treated, leaving some (potentially multivariate) functions of the covariates with different means between the two groups.
• When these "imbalanced" functions influence the non-treatment potential outcome, the conditioning on observed covariates fails, and ATT estimates may be biased.
• Kernel balancing, introduced here, targets a weaker requirement for unbiased ATT estimation, specifically, that the expected non-treatment potential outcome for the treatment and control groups are equal.