A graphical model is a graph that represents a set of conditional independence relations among the vertices (random variables). The graph is often given a causal interpretation as well. I describe how graphical causal models can be used in an algorithm for constructing partial information about causal graphs from observational data that is reliable in the large sample limit, even when some of the variables in the causal graph are unmeasured. I also describe an algorithm for estimating from observational data (in some cases) the total effect of a given variable on a second variable, and theoretical insights into fundamental limitations on the possibility of certain causal inferences by any algorithm whatsoever, and regardless of sample size....
In several fields, sample data are observed at discrete instead of continuous levels. For example, i...
In several fields, sample data are observed at discrete instead of continuous levels. For example, i...
In several fields, sample data are observed at discrete instead of continuous levels. For example, i...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Abstract: From their inception, causal systems models (more commonly known as structural-equations m...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Graphical models are a powerful tool for causal model specification. Besides al-lowing for a hierarc...
abstract. The development of macro-econometrics has been per-sistently fraught with a tension betwee...
Abstract: "We unify two contemporary theoretical frameworks for representing causal dependencies. Di...
We describe an approach to learning causal models that leverages temporal information. We posit the ...
This article (which is mainly expository) sets up graphical models for causation, having a bit less ...
Causal inference is a science with multi-disciplinary evolution and applications. On the one hand, i...
Graphical models in statistics and econometrics provide capability to describe causal relations usin...
Critical to reliable prediction and causal inference is understanding structural relationships in th...
In several fields, sample data are observed at discrete instead of continuous levels. For example, i...
In several fields, sample data are observed at discrete instead of continuous levels. For example, i...
In several fields, sample data are observed at discrete instead of continuous levels. For example, i...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Abstract: From their inception, causal systems models (more commonly known as structural-equations m...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
The primary aim of this paper is to show how graphical models can be used as a mathematical language...
Graphical models are a powerful tool for causal model specification. Besides al-lowing for a hierarc...
abstract. The development of macro-econometrics has been per-sistently fraught with a tension betwee...
Abstract: "We unify two contemporary theoretical frameworks for representing causal dependencies. Di...
We describe an approach to learning causal models that leverages temporal information. We posit the ...
This article (which is mainly expository) sets up graphical models for causation, having a bit less ...
Causal inference is a science with multi-disciplinary evolution and applications. On the one hand, i...
Graphical models in statistics and econometrics provide capability to describe causal relations usin...
Critical to reliable prediction and causal inference is understanding structural relationships in th...
In several fields, sample data are observed at discrete instead of continuous levels. For example, i...
In several fields, sample data are observed at discrete instead of continuous levels. For example, i...
In several fields, sample data are observed at discrete instead of continuous levels. For example, i...