  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. Another
important feature of SVM is that the regularization parameter controls the
trade-off between covariate balance and effective sample size. As a result, the
existing SVM path algorithm can be used to compute the balance-sample size
frontier. We characterize the bias of causal effect estimation arising from
this trade-off, connecting the proposed SVM procedure to the existing kernel
balancing methods. Finally, we conduct simulation and empirical studies to
evaluate the performance of the proposed methodology and find that SVM is
competitive with the state-of-the-art covariate balancing methods.
