Estimating Average Treatment Effects with Support Vector Machines

Understand

Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature.

• We demonstrate that SVM can be used to balance covariates and estimate average causal effects under the unconfoundedness assumption.
• Specifically, we adapt the SVM classifier as a kernel-based weighting procedure that minimizes the maximum mean discrepancy between the treatment and control groups while simultaneously maximizing effective sample size.
• We also show that SVM is a continuous relaxation of the quadratic integer program for computing the largest balanced subset, establishing its direct relation to the cardinality matching method.