Results 11  20
of
27
The New Challenge: From a Century of Statistics to an Age of Causation
 COMPUTING SCIENCE AND STATISTICS
, 1997
"... Some of the main users of statistical methods  economists, social scientists, and epidemiologists  are discovering that their fields rest not on statistical but on causal foundations. The blurring of these foundations over the years follows from the lack of mathematical notation capable of disti ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
Some of the main users of statistical methods  economists, social scientists, and epidemiologists  are discovering that their fields rest not on statistical but on causal foundations. The blurring of these foundations over the years follows from the lack of mathematical notation capable of distinguishing causal from equational relationships. By providing formal and natural explication of such relations, graphical methods have the potential to revolutionize how statistics is used in knowledgerich applications. Statisticians, in response, are beginning to realize that causality is not a metaphysical deadend but a meaningful concept with clear mathematical underpinning. The paper surveys these developments and outlines future challenges.
Statistics and Causal Inference: A Review
, 2003
"... This paper aims at assisting empirical researchers benefit from recent advances in causal inference. 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 assump ..."
Abstract

Cited by 11 (6 self)
 Add to MetaCart
This paper aims at assisting empirical researchers benefit from recent advances in causal inference. 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 formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, the assessment of causal effects, the interpretation of counterfactuals, and a symbiosis between counterfactual and graphical methods of analysis.
Identifying linear causal effects
 In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI
, 2004
"... This paper concerns the assessment of linear causeeffect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and t ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
This paper concerns the assessment of linear causeeffect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Using this approach the paper develops a systematic procedure for recognizing identifiable direct causal effects.
Identifying Conditional Causal Effects
 In Conference on Uncertainty in Artificial Intelligence (UAI
, 2004
"... This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects be ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
This paper concerns the assessment of the effects of actions from a combination of nonexperimental data and causal assumptions encoded in the form of a directed acyclic graph in which some variables are presumed to be unobserved. We provide a procedure that systematically identifies cause effects between two sets of variables conditioned on some other variables, in time polynomial in the number of variables in the graph. The identifiable conditional causal effects are expressed in terms of the observed joint distribution. 1
Causal Inference in the Health Sciences: A Conceptual Introduction
 Health Services and Outcomes Research Methodology
, 2001
"... This paper provides a conceptual introduction to causal inference, aimed to assist health services researchers benefit 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 multivari ..."
Abstract

Cited by 7 (0 self)
 Add to MetaCart
This paper provides a conceptual introduction to causal inference, aimed to assist health services researchers benefit 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 underlie all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, corrections for noncompliance, and a symbiosis between counterfactual and graphical methods of analysis.
Signed directed acyclic graphs for causal inference
, 2010
"... Formal rules governing signed edges on causal directed acyclic graphs are described and it is shown how these rules can be useful in reasoning about causality. Specifically, the notions of a monotonic effect, a weak monotonic effect and a signed edge are introduced. Results are developed relating t ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
Formal rules governing signed edges on causal directed acyclic graphs are described and it is shown how these rules can be useful in reasoning about causality. Specifically, the notions of a monotonic effect, a weak monotonic effect and a signed edge are introduced. Results are developed relating these monotonic effects and signed edges to the sign of the causal effect of an intervention in the presence of intermediate variables. The incorporation of signed edges in the directed acyclic graph causal framework furthermore allows for the development of rules governing the relationship between monotonic effects and the sign of the covariance between two variables. It is shown that when certain assumptions about monotonic effects can be made then these results can be used to draw conclusions about the presence of causal effects even when data are missing on confounding variables.
A study of identifiability in causal Bayesian network
, 2006
"... This paper addresses the problem of identifying causal effects from nonexperimental data in a causal Bayesian network, i.e., a directed acyclic graph that represents causal relationships. The identifiability question asks whether it is possible to compute the probability of some set of (effect) vari ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
This paper addresses the problem of identifying causal effects from nonexperimental data in a causal Bayesian network, i.e., a directed acyclic graph that represents causal relationships. The identifiability question asks whether it is possible to compute the probability of some set of (effect) variables given intervention on another set of (intervention) variables, in the presence of nonobservable (i.e., hidden or latent) variables. It is well known that the answer to the question depends on the structure of the causal Bayesian network, the set of observable variables, the set of effect variables, and the set of intervention variables. Our work is based on the work of Tian and Pearl [1, 2, 3] and our own work [4], and extends it. We show that the identify algorithm that Tian and Pearl define and prove sound for semiMarkovian models can be transfered to general causal graphs and is not only sound, but also complete. This result effectively solves the identifiability question for causal Bayesian networks that Pearl posed in 1995 [5], by providing a sound and complete algorithm for identifiability. 1
Causal inference by surrogate experiments: zidentifiability
, 2012
"... We address the problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z, that is more accessible to manipulation. This problem, which we call zidentifiability, reduces to ordinary identifiability when Z = ∅ and, like the latter, can be given sy ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
We address the problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z, that is more accessible to manipulation. This problem, which we call zidentifiability, reduces to ordinary identifiability when Z = ∅ and, like the latter, can be given syntactic characterization using the docalculus [Pearl, 1995; 2000]. We provide a graphical necessary and sufficient condition for zidentifiability for arbitrary sets X, Z, and Y (the outcomes). We further develop a complete algorithm for computing the causal effect of X on Y using information provided by experiments on Z. Finally, we use our results to prove completeness of docalculus relative to zidentifiability, a result that does not follow from completeness relative to ordinary identifiability.
Metatransportability of causal effects: A formal approach
, 2013
"... This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a different environment, in which only passive observations can be collected. Pearl and Bareinboim (2011) established a complete characterization for such transfer between two domain ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a different environment, in which only passive observations can be collected. Pearl and Bareinboim (2011) established a complete characterization for such transfer between two domains, a source and a target, and this paper generalizes their results to multiple heterogeneous domains. It establishes a necessary and sufficient condition for deciding when effects in the target domain are estimable from both statistical and causal information transferred from the experiments in the source domains. The paper further provides a complete algorithm for computing the transport formula, that is, a way of fusing observational and experimental information to synthesize an unbiased estimate of the desired effects.
mTransportability: Transportability of a Causal Effect from Multiple Environments
"... {shlee, honavar} at iastate.edu We study mtransportability, a generalization of transportability, which offers a license to use causal information elicited from experiments and observations in m ≥ 1 source environments to estimate a causal effect in a given target environment. We provide a novel ch ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
{shlee, honavar} at iastate.edu We study mtransportability, a generalization of transportability, which offers a license to use causal information elicited from experiments and observations in m ≥ 1 source environments to estimate a causal effect in a given target environment. We provide a novel characterization of mtransportability that directly exploits the completeness of docalculus to obtain the necessary and sufficient conditions for mtransportability. We provide an algorithm for deciding mtransportability that determines whether a causal relation is mtransportable; and if it is, produces a transport formula, that is, a recipe for estimating the desired causal effect by combining experimental information from m source environments with observational information from the target environment. 1