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Trygve Haavelmo and the Emergence of Causal Calculus
, 2012
"... Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. Th ..."
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Haavelmo was the first to recognize the capacity of economic models to guide policies. This paper describes some of the barriers that Haavelmo’s ideas have had (and still have) to overcome, and lays out a logical framework for capturing the relationships between theory, data and policy questions. The mathematical tools that emerge from this framework now enable investigators to answer complex policy and counterfactual questions using embarrassingly simple routines, some by mere inspection of the model’s structure. Several such problems are illustrated by examples, including misspecification tests, identification, mediation and introspection. Finally, we observe that modern economists are largely unaware of the benefits that Haavelmo’s ideas bestow upon them and, as a result, econometric research has not fully utilized modern advances in causal analysis. 1
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 15 (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 Past as Future: The Marshallian Approach to Post Walrasian Econometrics
 Post Walrasian Macroeconomics: Beyond the Dynamic Stochastic General Equilibrium Model
, 2006
"... I. PostWalrasian Econometrics The popular image of the scientific revolution usually pits young revolutionaries against old conservatives. Freeman Dyson (2004, p. 16) observes that, in particle physics in the mid20th century, something had to change. But in the revolution of quantum electrodynamic ..."
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Cited by 14 (4 self)
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I. PostWalrasian Econometrics The popular image of the scientific revolution usually pits young revolutionaries against old conservatives. Freeman Dyson (2004, p. 16) observes that, in particle physics in the mid20th century, something had to change. But in the revolution of quantum electrodynamics, Einstein, Dirac, Heisenberg, Born, and Schödinger were old revolutionaries, while the winners, Feynman, Schwinger, and Tomonaga, were young conservatives. PostWalrasian economics is not a doctrine, but a slogan announcing that something has to change. Most of the selfconscious efforts to forge a postWalrasian economics are due to old radicals. Here I want to explore the space of the young conservative: the future is past, particularly in the methodology of Alfred Marshall’s methodological essay, “The Present Position of Economics ” (1885). The radical approach identifies the problem as Walrasian theory and seeks to replace it with something better and altogether different. The conservative approach says that theory is not the problem. The problem is rather to establish an empirical discipline that connects theory to the world.
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 ..."
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Cited by 14 (0 self)
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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.
Constraints and nonconstraints in causal learning: Reply
 Psychological Review
, 2005
"... The task of causal learning concerns figuring out the laws that govern how the world works. The goal of a reasoner who engages in this task is to gain an understanding of the empirical world that would guide decisions regarding actions to achieve the reasoner’s objectives. The comments by P. A. Whit ..."
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Cited by 13 (5 self)
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The task of causal learning concerns figuring out the laws that govern how the world works. The goal of a reasoner who engages in this task is to gain an understanding of the empirical world that would guide decisions regarding actions to achieve the reasoner’s objectives. The comments by P. A. White (2005)
Foundations for Bayesian networks
, 2001
"... Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probabi ..."
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Cited by 11 (7 self)
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Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current foundations are problematic, and put forward new foundations which involve aspects of both the interpreted and the formal approaches. One standard approach is to interpret a Bayesian network objectively: the graph in a Bayesian network represents causality in the world and the specified probabilities are objective, empirical probabilities. Such an interpretation founders when the Bayesian network independence assumption (often called the causal Markov condition) fails to hold. In §2 I catalogue the occasions when the independence assumption fails, and show that such failures are pervasive. Next, in §3, I show that even where the independence assumption does hold objectively, an agent’s causal knowledge is unlikely to satisfy the assumption with respect to her subjective probabilities, and that slight differences between an agent’s subjective Bayesian network and an objective Bayesian network can lead to large differences between probability distributions determined by these networks. To overcome these difficulties I put forward logical Bayesian foundations in §5. I show that if the graph and probability specification in a Bayesian network are thought of as an agent’s background knowledge, then the agent is most rational if she adopts the probability distribution determined by the
Toward a unified theory of causality
 Comparative Political Studies
, 2008
"... In comparative research, analysts conceptualize causation in contrasting ways when they pursue explanation in particular cases (caseoriented research) versus large populations (populationoriented research). With caseoriented research, they understand causation in terms of necessary, sufficient, I ..."
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In comparative research, analysts conceptualize causation in contrasting ways when they pursue explanation in particular cases (caseoriented research) versus large populations (populationoriented research). With caseoriented research, they understand causation in terms of necessary, sufficient, INUS, and SUIN causes. With populationoriented research, by contrast, they understand causation as mean causal effects. This article explores whether it is possible to translate the kind of causal language that is used in caseoriented research into the kind of causal language that is used in populationoriented research (and vice versa). The article suggests that such translation is possible, because certain types of INUS causes manifest themselves as variables that exhibit partial effects when studied in populationoriented research. The article concludes that the conception of causation adopted in caseoriented research is appropriate for the population level, whereas the conception of causation used in populationoriented research is valuable for making predictions in the face of uncertainty.
Explaining disease: correlations, causes, and mechanisms
 Minds and Machines
, 1998
"... Abstract. Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose ..."
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Abstract. Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical assignment of factors to the patient whose disease is being explained. This paper first discusses inference from correlation to causation, integrating recent psychological discussions of causal reasoning with epidemiological approaches to understanding disease causation, particularly concerning ulcers and lung cancer. It then shows how causal mechanisms represented by causal networks can contribute to reasoning involving correlation and causation. The understanding of causation and causal mechanisms provides the basis for a presentation of the causal network instantiation model of medical explanation.
An Introduction to Causal Inference
 Causality in Crisis? University of Notre Dame
, 1997
"... developed a theory of statistical causal inference. In his presentation at the Notre Dame ..."
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developed a theory of statistical causal inference. In his presentation at the Notre Dame