Causal statistical inference in high dimensions

Peter Bühlmann ()

Mathematical Methods of Operations Research, 2013, vol. 77, issue 3, 357-370

Abstract: We present a short selective review of causal inference from observational data, with a particular emphasis on the high-dimensional scenario where the number of measured variables may be much larger than sample size. Despite major identifiability problems, making causal inference from observational data very ill-posed, we outline a methodology providing useful bounds for causal effects. Furthermore, we discuss open problems in optimization, non-linear estimation and for assigning statistical measures of uncertainty, and we illustrate the benefits and limitations of high-dimensional causal inference for biological applications. Copyright Springer-Verlag 2013

Keywords: Directed acyclic graphs; Intervention calculus (do-operator); Graphical modeling; Observational data; PC-algorithm (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:mathme:v:77:y:2013:i:3:p:357-370

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DOI: 10.1007/s00186-012-0404-7

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