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dc.contributor.author Wang, Alex en_US
dc.date.accessioned 2009-09-16T15:35:02Z
dc.date.available 2009-09-16T15:35:02Z
dc.date.issued 2009 en_US
dc.identifier.uri http://hdl.handle.net/10161/1422
dc.description Honors thesis, Department of Mathematics en_US
dc.description.abstract This thesis aims to show that explicit understanding of possible causal structures often aids in inferring the true causes from data. This is done by first understanding that causes are chains of counterfactual dependence. Insofar as experiments, active or natural are not perfect, data can easily support false counterfactuals. Even those tools especially designed to identify unbiased estimates, like instrumental variables, often fail. Causal structure explains the failure of these tools, but more importantly allows us to better identify which counterfactuals to reject or accept. en_US
dc.format.extent 227792 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.title Causal Inference and Understanding Causal Structure en_US
dc.department Mathematics en_US

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