نتایج جستجو برای: descriptave and causal methods

تعداد نتایج: 16912397  

2002
Judea Pearl

This paper provides a conceptual introduction to causal inference, aimed to assist researchers bene t from recent advances in this area. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in ...

2006
Peter A Hall Minda de Gunzburg

Challenging the contention that statistical methods applied to large numbers of cases invariably provide better grounds for causal inference, this article explores the value of a method of systematic process analysis that can be applied in a small number of cases. It distinguishes among three modes of explanation – historically specific, multivariate, and theory-oriented – and argues that syste...

2001
Judea Pearl

This paper provides a conceptual introduction to causal inference, aimed to assist researchers bene t from recent advances in this area. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in ...

Journal: :Annual Review of Statistics and Its Application 2019

Journal: :Classical and Quantum Gravity 2005

سجادی, سید علیرضا, علی محمدیان, معصومه, محمدی, ندا, منصورنیا, محمدعلی, پوستچی, حسین, یاسری, مهدی,

One of the traditional methods used for the analysis of survival data is the Cox regression technique. This method calculates the conditional risk ratio. However, when the aim of the study is to estimate the effect of exposure in the total population level, using these conditional methods is not apposite. Furthermore, the hazard ratio has disadvantages of its own such as being non-collapsible, ...

2014
Ricardo Silva Robin J. Evans

One of the most fundamental problems in causal inference is the estimation of a causal effect when treatment and outcome are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest “weak” pa...

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