How Coarsening Simplifies Matching-Based Causal Inference Theory

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Abstract:

The simplicity and power of matching methods have made them an increasingly popular approach to causal inference in observational data. Existing theories that justify these techniques are well developed but either require exact matching, which is usually infeasible in practice, or sacrifice some simplicity via asymptotic theory, specialized bias corrections, and novel variance estimators; and extensions to approximate matching with multicategory treatments have not yet appeared. As an alternative, we show how conceptualizing continuous variables as having logical breakpoints (such as phase transitions when measuring temperature or high school or college degrees in years of education) is both natural substantively and can be used to simplify causal inference theory. The result is a finite sample theory that is widely applicable, simple to understand, and easy to implement by using matching to preprocess the data, after which one can use whatever method would have been applied without matching. The theoretical simplicity also allows for binary, multicategory, and continuous treatment variables from the start and for extensions to valid inference under imperfect treatment assignment.