Anda belum login :: 23 Nov 2024 05:48 WIB
Detail
ArtikelGraphical Models For Causation, And The Identification Problem  
Oleh: Freedman, David A.
Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Evaluation Review vol. 28 no. 4 (Aug. 2004), page 267-293.
Topik: causation; linear models; graphical models; identification; invariance under intervention
Fulltext: 267.pdf (192.76KB)
Isi artikelThis article (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted usingm conditional distributions, so thatwe can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are fewsuccessful applications of graphicalmodels, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
Opini AndaKlik untuk menuliskan opini Anda tentang koleksi ini!

Kembali
design
 
Process time: 0.015625 second(s)