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Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example
2014
Conference on Uncertainty in Artificial Intelligence
Estimating the strength of causal effects from observational data is a common problem in scientific research. A popular approach is based on exploiting observed conditional independences between variables. It is wellknown that this approach relies on the assumption of faithfulness. In our opinion, a more important practical limitation of this approach is that it relies on the ability to distinguish independences from (arbitrarily weak) dependences. We present a simple analysis, based on purely
dblp:conf/uai/CorniaM14
fatcat:jmkyzbtvmrcjhjgvpcnanhnd4q