Estimation of causal effects using linear non-Gaussian causal models with hidden variables
نویسندگان
چکیده
منابع مشابه
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
The task of estimating causal effects from non-experimental data is notoriously difficult and unreliable. Nevertheless, precisely such estimates are commonly required in many fields including economics and social science, where controlled experiments are often impossible. Linear causal models (structural equation models), combined with an implicit normality (Gaussianity) assumption on the data,...
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The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous work has, however, made an explicit or implicit assumption of gaussianity, limiting the identifiability of the models. We have recently shown (Shimizu et al., 2005; Hoyer et al., 2006) that for non-gaussian distributions...
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In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data (Spirtes et al. 2000; Pearl 2000). Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, we show how to discover the complete causal structure of continuous-valued da...
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In many fields of science researchers are faced with the problem of estimating causal effects from non-experimental data. A key issue is to avoid inconsistent estimators due to confounding by measured or unmeasured covariates, a problem commonly solved by ‘adjusting for’ a subset of the observed variables. When the data generating process can be represented by a directed acyclic graph, and this...
متن کاملSupplementary Material for “Statistical test for consistent estimation of causal effects in linear non-Gaussian models”
This document contains supplementary material to the article ‘Statistical test for consistent estimation of causal effects in linear non-Gaussian models’, AISTATS 2012. A table of contents is given below.
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2008
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2008.02.006