Causal inferences from many experiments

Research output: Contribution to journalArticlepeer-review

Abstract

The underlying statistical concept that animates empirical strategies for extracting causal inferences from observational data is that observational data may be adjusted to resemble data that might have originated from a randomized experiment. This idea has driven the literature on matching methods. We explore an un-mined idea for making causal inferences with observational data–that any given observational study may contain a large number of indistinguishably balanced matched designs. We demonstrate how the absence of a unique best solution presents an opportunity for greater information retrieval in causal inference analysis based on the principle that many solutions teach us more about a given scientific hypothesis than a single study and improves our discernment with observational studies. The implementation can be achieved by integrating the statistical theories and models within a computational optimization framework that embodies the statistical foundations and reasoning.

Original languageEnglish (US)
Pages (from-to)2908-2922
Number of pages15
JournalJournal of Applied Statistics
Volume44
Issue number16
DOIs
StatePublished - Dec 10 2017

Keywords

  • Causal inference
  • computational methods
  • operations research
  • optimization
  • subset selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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