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. We show that the SVM cost parameter controls the trade-off between covariate balance and subset size, and as a result, existing SVM regularization path algorithms can be used to compute the balance-sample size frontier. We then characterize the bias of causal effect estimation arising from this tradeoff, 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.