The Balance-Sample Size Frontier in Matching Methods for Causal Inference
| Autor: | Gary King, Chris Lucas, Richard A. Nielsen |
|---|---|
| Přispěvatelé: | Massachusetts Institute of Technology. Department of Political Science, Nielsen, Richard Alexander |
| Rok vydání: | 2016 |
| Předmět: |
Balance (metaphysics)
Mathematical optimization Matching (statistics) Similarity (geometry) Sociology and Political Science Computer science 05 social sciences Control (management) 01 natural sciences 0506 political science Set (abstract data type) 010104 statistics & probability Sample size determination Causal inference Political Science and International Relations Statistics 050602 political science & public administration 0101 mathematics Tweaking |
| Zdroj: | Other repository |
| Popis: | We propose a simplified approach to matching for causal inference that simultaneously optimizes balance (similarity between the treated and control groups) and matched sample size. Existing approaches either fix the matched sample size and maximize balance or fix balance and maximize sample size, leaving analysts to settle for suboptimal solutions or attempt manual optimization by iteratively tweaking their matching method and rechecking balance. To jointly maximize balance and sample size, we introduce the matching frontier, the set of matching solutions with maximum possible balance for each sample size. Rather than iterating, researchers can choose matching solutions from the frontier for analysis in one step. We derive fast algorithms that calculate the matching frontier for several commonly used balance metrics. We demonstrate this approach with analyses of the effect of sex on judging and job training programs that show how the methods we introduce can extract new knowledge from existing data sets. |
| Databáze: | OpenAIRE |
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