We show how to use matching in causal inference to ameliorate model dependence -- where small, indefensible changes in model specification have large impacts on our conclusions. We introduce matching methods that are simpler, more powerful, and easier to understand than existing approaches. We also show that the most commonly used existing method, propensity score matching, should rarely be used in practice. Easy-to-use software is available to implement all methods discussed.