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Causal Diagrams For Empirical Research
"... The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subjectmatter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if ..."
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Cited by 219 (37 self)
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The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subjectmatter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in terms of observed distributions; otherwise, the diagrams can be queried to suggest additional observations or auxiliary experiments from which the desired inferences can be obtained. Key words: Causal inference, graph models, interventions treatment effect 1 Introduction The tools introduced in this paper are aimed at helping researchers communicate qualitative assumptions about causeeffect relationships, elucidate the ramifications of such assumptions, and derive causal inferences from a combination...
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 ..."
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Cited by 12 (6 self)
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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.
The Foundations of Causal Inference
 SUBMITTED TO SOCIOLOGICAL METHODOLOGY.
, 2010
"... This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of ..."
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Cited by 11 (3 self)
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This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 195060s, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring the effects of potential interventions (also called “causal effects” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and nonlinear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and
Information Fusion, Causal Probabilistic Network And Probanet II: Inference Algorithms and Probanet System
 Proc. 1st Intl. Workshop on Image Analysis and Information Fusion
, 1997
"... As an extension of an overview paper [Pan and McMichael, 1997] on information fusion and Causal Probabilistic Networks (CPN), this paper formalizes kernel algorithms for probabilistic inferences upon CPNs. Information fusion is realized through updating joint probabilities of the variables upon the ..."
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Cited by 3 (2 self)
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As an extension of an overview paper [Pan and McMichael, 1997] on information fusion and Causal Probabilistic Networks (CPN), this paper formalizes kernel algorithms for probabilistic inferences upon CPNs. Information fusion is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematicsoriented literatures, with gaps lled in with regards to computability and completeness. In particular, possible optimizations on causal tree algorithm, graph triangulation and junction tree algorithm are discussed. Probanet has been designed and developed as a generic shell, or say, mother system for CPN construction and application. The design aspects and current status of Probanet are described. A few directions for research and system development are pointed out, including hierarchical structuring of network, structure decomposition and adaptive inference algorithms. This paper thus has a nature of integration including literature review, algorithm formalization and future perspective.
On The Identification Of Nonparametric Structural Models
, 1997
"... In this paper we study the identifiability of nonparametric models, that is, models in which both the functional forms of the equations and the probability distributions of the disturbances remain unspecified. Identifiability in such models does not mean uniqueness of parameters but rather uniquenes ..."
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Cited by 2 (1 self)
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In this paper we study the identifiability of nonparametric models, that is, models in which both the functional forms of the equations and the probability distributions of the disturbances remain unspecified. Identifiability in such models does not mean uniqueness of parameters but rather uniqueness of the set of predictions of interest to the investigator. For example, predicting the effects of changes, interventions, and control. We provide sufficient and necessary conditions for identifying a set of causal predictions of the type: "Find the distribution of Y , assuming that X is controlled by external intervention", where Y and X are arbitrary variables of interest. Whenever identifiable, such predictions can be expressed in closed algebraic form, in terms of observed distributions. We also show how the identifying criteria can be verified qualitatively, by inspection, using the graphical representation of the structural model. When compared to standard identifiability tests of lin...
3 THE FOUNDATIONS OF CAUSAL INFERENCE
"... This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating, and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM)—a natural generalization of ..."
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This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating, and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM)—a natural generalization of those used by econometricians and social scientists in the 1950s and 1960s, which provides a coherentmathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring the effects of potential interventions (also called “causal effects ” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and nonlinear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and potentialoutcome frameworks, and develops symbiotic tools that use the strong features of both.
(Draft Copy) On the Statistical Interpretation of Structural Equations
"... F28.92> y 2 and x 1 were fixed" using the model described in (1), the result does not match the interpretation advanced by Goldberger. Specifically, assuming u 1 and u 2 are zeromean disturbances (independent on X 1 and X 2 ), Wermuth finds E(Y 1 j Y 2 = y 2 ; X 1 = x 1 ) 6= a 1 y 2 + a 2 ..."
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F28.92> y 2 and x 1 were fixed" using the model described in (1), the result does not match the interpretation advanced by Goldberger. Specifically, assuming u 1 and u 2 are zeromean disturbances (independent on X 1 and X 2 ), Wermuth finds E(Y 1 j Y 2 = y 2 ; X 1 = x 1 ) 6= a 1 y 2 + a 2 x 1 (unless further assumptions are made) and concludes that "the parameters in (1) cannot have the meaning Arthur Goldberger claims they have." 1 This exchange between a statistician and an economist exemplifies the long history of tension between regression analysis and structural equations modeling, which dates back to the inception of the latter by Wright [27],
Causal Relationship Between Indicators of Human Health, the Environment and Socioeconomic Variables for the OECD Countries
, 1999
"... There has been a lot of debate regarding the impact of emissions of pollutants on human health and the environment. Epidemiological studies tend to show the impact of increased ambient concentrations of pollutants on increased hospital admissions, mortality, morbidity, respiratory problems, etc. Wit ..."
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There has been a lot of debate regarding the impact of emissions of pollutants on human health and the environment. Epidemiological studies tend to show the impact of increased ambient concentrations of pollutants on increased hospital admissions, mortality, morbidity, respiratory problems, etc. Without controlled experiments that compare people who are exposed to contaminants to those who are not, it is impossible to predict the causes and effects with certainty. Nevertheless, estimates of human and environmental health benefits from improved air quality indicate that there are associations between ambient concentrations of contaminants, human health and environmental impacts. The present study examines the linkages between human health, environmental quality, and emission of pollutants and selected socioeconomic variables for selected OECD regions. Path or causal models will be constructed using health, socioeconomic and environmental parameters to determine the direction of causal relationships, their magnitude and possible implication for public policy making. This analysis will be performed for the OECD countries, and selected regions of the OECD (North America, the Pacific Rim, and Europe). Comparative analysis of the relationships between human health, socioeconomic and environmental variables among the OECD countries will indicate, among other things, i) whether or not environmental quality is an important determinant of human health, ii) whether or not spending on health care system is significantly influenced by indicators of health status that are included by environmental variables, and iii) which socioeconomic variables are significantly associated with indicators of human and the environment health.
forthcoming in The Oxford Handbook of Political Economy
, 2006
"... The people have been promised more than can be promised; they have been given hopes that it will be impossible to realize... The expenses of the new regime will actually be heavier than the old. And in the last analysis the people will judge the revolution by this fact alone does it take more or le ..."
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The people have been promised more than can be promised; they have been given hopes that it will be impossible to realize... The expenses of the new regime will actually be heavier than the old. And in the last analysis the people will judge the revolution by this fact alone does it take more or less money? Are they better o ¤ ? Do they have more work? And is that work better paid? Mirabeau [Honoré Gabriel Riquetti] (1791) All Political history shows that the standing of the Government and its ability to hold the con
dence of the electorate at a General Election depend on the success of its economic policy. Harold Wilson (1968)
(Draft Copy) On the Statistical Interpretation of Structural Equations
"... an economist might arrive at a model like this: ..."
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