Results 1  10
of
18
On specifying graphical models for causation, and the identification problem
 Evaluation Review
, 2004
"... This paper (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 c ..."
Abstract

Cited by 19 (2 self)
 Add to MetaCart
This paper (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 using conditional distributions, so that we 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 few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
Statistical models for causation: what inferential leverage do they provide?”Evaluation Review
, 2006
"... ..."
Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies
, 2004
"... ..."
Confounding Equivalence in Observational Studies (or, when are two measurements equally valuable for effect estimation?)
, 2009
"... ..."
Confounding Equivalence in Causal Inference
 PROCEEDINGS OF UAI, 433441. AUAI, CORVALLIS, OR, 2010.
, 2010
"... The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same biasreducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e., satisfy the backdoor criteri ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same biasreducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e., satisfy the backdoor criterion) or (2) the Markov boundaries surrounding the manipulated variable(s) are identical in both sets. Applications to covariate selection and model testing are discussed.
Statistical Models for Causation
, 2005
"... We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally probl ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally problematic. Therefore, causal relationships are seldom to be inferred from a data set by running statistical algorithms, unless there is substantial prior knowledge about the mechanisms that generated the data. We begin with linear models (regression analysis) and then turn to graphical models, which may in principle be nonlinear.
Linear Statistical Models for Causation: A Critical Review
"... Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later ..."
Abstract
 Add to MetaCart
Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later
Ignorable Common Information, Null Sets and Basu’s First Theorem
"... This paper deals with the Intersection Property, or Basu’s First Theorem, which is valid under a condition of no common information, also known as measurable separability. After formalizing this notion, the paper reviews general properties and give operational characterizations in two topical cases: ..."
Abstract
 Add to MetaCart
This paper deals with the Intersection Property, or Basu’s First Theorem, which is valid under a condition of no common information, also known as measurable separability. After formalizing this notion, the paper reviews general properties and give operational characterizations in two topical cases: the finite one and the multivariate normal one. The paper concludes discussing the relevance of these characterizations for different fields as graphical models, zero entries in contingency tables, causal analysis and estimability in Markov processes.
Judea Pearl * and Azaria Paz Confounding Equivalence in Causal Inference
"... Abstract: The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same biasreducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e. satisfy the backdoo ..."
Abstract
 Add to MetaCart
Abstract: The paper provides a simple test for deciding, from a given causal diagram, whether two sets of variables have the same biasreducing potential under adjustment. The test requires that one of the following two conditions holds: either (1) both sets are admissible (i.e. satisfy the backdoor criterion) or (2) the Markov boundaries surrounding the treatment variable are identical in both sets. We further extend the test to include treatmentdependent covariates by broadening the backdoor criterion and establishing equivalence of adjustment under selection bias conditions. Applications to covariate selection and model testing are discussed.