Separators and adjustment sets in causal graphs: Complete criteria and an algorithmic framework
- Zander, Benito van der
- Liskiewicz, Maciej
- Textor, Johannes C.
DOIAbstract
Contains fulltext : 202853.pdf (Publisher’s version ) (Open Access
Contains fulltext : 202853.pdf (Publisher’s version ) (Open Access
yz Causal discovery, for the most part, is concerned with learning causal models in the form of dire...
This paper concerns the assessment of the eects of actions or poli-cies from a combination of: (i) n...
We propose a framework for building graphical decision models from individual causal mechanisms. Our...
In practice the vast majority of causal effect estimations from observational data are computed usin...
We consider estimation of a total causal effect from observational data via covariate adjustment. Id...
Covariate adjustment is a widely used approach to estimate total causal effects from observational d...
Covariate adjustment is a widely used approach to estimate total causal effects from observational d...
Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence...
Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence...
Causal graphs are essential tools to find sufficient adjustment sets in observational studies. Subje...
We present a graphical criterion for covariate adjustment that is sound and complete for four differ...
We consider the problem of identifying a conditional causal effect through covariate adjustment. We ...
Dependency knowledge of the form "x is independent of y once z is known" invariably obeys ...
Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, ...
Contains fulltext : 91907.pdf (author's version ) (Open Access)27th Conference on ...
yz Causal discovery, for the most part, is concerned with learning causal models in the form of dire...
This paper concerns the assessment of the eects of actions or poli-cies from a combination of: (i) n...
We propose a framework for building graphical decision models from individual causal mechanisms. Our...
In practice the vast majority of causal effect estimations from observational data are computed usin...
We consider estimation of a total causal effect from observational data via covariate adjustment. Id...
Covariate adjustment is a widely used approach to estimate total causal effects from observational d...
Covariate adjustment is a widely used approach to estimate total causal effects from observational d...
Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence...
Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence...
Causal graphs are essential tools to find sufficient adjustment sets in observational studies. Subje...
We present a graphical criterion for covariate adjustment that is sound and complete for four differ...
We consider the problem of identifying a conditional causal effect through covariate adjustment. We ...
Dependency knowledge of the form "x is independent of y once z is known" invariably obeys ...
Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, ...
Contains fulltext : 91907.pdf (author's version ) (Open Access)27th Conference on ...
yz Causal discovery, for the most part, is concerned with learning causal models in the form of dire...
This paper concerns the assessment of the eects of actions or poli-cies from a combination of: (i) n...
We propose a framework for building graphical decision models from individual causal mechanisms. Our...
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