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From association to causation via regression
 Indiana: University of Notre Dame
, 1997
"... For nearly a century, investigators in the social sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. In my view, this enterprise has not been successful. The models tend ..."
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Cited by 29 (7 self)
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For nearly a century, investigators in the social sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. In my view, this enterprise has not been successful. The models tend to neglect the difficulties in establishing causal relations, and the mathematical complexities tend to obscure rather than clarify the assumptions on which the analysis is based. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, if A, B, C,... remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work a principle honored more often in the breach than the observance.
Causality and Price Discovery: An Application of Directed Acyclic Graphs
, 2002
"... Directed Acyclic Graphs (DAG's) and Error Correction Models (ECM's) are employed to analyze questions of price discovery between spatially separated commodity markets and the transportation market linking them together. Results from our analysis suggest these markets are highly interconnec ..."
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Cited by 17 (5 self)
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Directed Acyclic Graphs (DAG's) and Error Correction Models (ECM's) are employed to analyze questions of price discovery between spatially separated commodity markets and the transportation market linking them together. Results from our analysis suggest these markets are highly interconnected but it is the inland commodity market that is strongly influenced by both the transportation and commodity export markets. However, the commodity markets affect the volatility of the transportation market over longer horizons. Our results suggest that transportation rates are critical in the price discovery process lending support for the recent development of exchange traded barge rate futures contracts.
Uncovering deterministic causal structures: a boolean approach. Synthese
, 2008
"... While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custombuilt for (nondeterministic) probabilistic structures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the p ..."
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Cited by 5 (3 self)
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While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custombuilt for (nondeterministic) probabilistic structures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or – in case of ambiguities – more than one causal structure is assigned to the minimalized deterministic dependencies.
1 The Maturity of Social Theory
"... Social theory is ordinarily not thought of as an autonomous academic field, a genuine and complete academic identity, or as an appropriate, or feasible, academic career choice, or as much more than an amorphous publishing category for books that are nonempirical, not strongly identified with a disc ..."
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Social theory is ordinarily not thought of as an autonomous academic field, a genuine and complete academic identity, or as an appropriate, or feasible, academic career choice, or as much more than an amorphous publishing category for books that are nonempirical, not strongly identified with a discipline, which make some “social ” reference. Sociologists typically think of it as the theoretical side of the discipline of sociology or as a subfield of sociology, thus a secondary identity that is submerged in, and subordinate to, the primary identity of “sociologist.” Nonsociologists think of it as a kind of supradisciplinary collection of themes traditionally associated with sociology or with left wing politics, and now associated with cultural studies and feminism. In view of this, the claim I wish to make is a somewhat startling claim. Social theory is not only a field but a mature one; one that is essentially complete and selfsufficient as a coherent and valuable form of intellectual activity: a voice in the conversation of mankind, with its own internal conversation of considerable complexity and depth. What I mean by mature field I do not propose to specify directly at the outset, though what I have in mind will become evident in what follows. It will suffice for now to think of a field in social terms, as a body of persons with not only common intellectual concerns, but a
Causality and Price Discovery: An Application of Directed Acyclic Graphs
, 2002
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Are There Algorithms That Discover Causal Structure? 30 June 1998
"... For nearly a century, investigators in the social and life sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. However, these formal procedures tend to neglect the diffic ..."
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For nearly a century, investigators in the social and life sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. However, these formal procedures tend to neglect the difficulties in establishing causal relations, and the mathematical complexities tend to obscure rather than clarify the assumptions on which the analysis is based. This paper focuses on statistical procedures that seem to convert association into causation. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, if A, B, C,...remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work—a principle honored more often in the breach than the observance. Spirtes, Glymour, and Scheines have developed algorithms for causal discovery. We have been quite critical of their work. Korb and Wallace, as well as SGS, have tried to answer the criticisms. This paper will continue the discussion. Their responses may lead to progress in clarifying assumptions behind the methods, but there is little progress in demonstrating that the assumptions hold true for any real applications. The mathematical theory may be of some interest, but claims to have developed a rigorous engine for inferring causation from association are premature at best. The theorems have no implications for samples of any realistic size. Furthermore, examples used to illustrate the algorithms are diagnostic of failure rather than success. There remains a wide gap between association and causation. 1.
Are There Algorithms That Discover Causal Structure? 30 June 1998
"... For nearly a century, investigators in the social and life sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. However, these formal procedures tend to neglect the diffic ..."
Abstract
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For nearly a century, investigators in the social and life sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. However, these formal procedures tend to neglect the difficulties in establishing causal relations, and the mathematical complexities tend to obscure rather than clarify the assumptions on which the analysis is based. This paper focuses on statistical procedures that seem to convert association into causation. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, ifA,B,C,...remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work—a principle honored more often in the breach than the observance. Spirtes, Glymour, and Scheines have developed algorithms for causal discovery. We have been quite critical of their work. Korb and Wallace, as well as SGS, have tried to answer the criticisms. This paper will continue the discussion. Their responses may lead to progress in clarifying assumptions behind the methods, but there is little progress in demonstrating that the assumptions hold true for any real applications. The mathematical theory may be of some interest, but claims to have developed a rigorous engine for inferring causation from association are premature at best. The theorems have no implications for samples of any realistic size. Furthermore, examples used to illustrate the algorithms are diagnostic of failure rather than success. There remains a wide gap between association and causation. 1.
THE CAUSAL CHAIN PROBLEM
"... This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertaining empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains a ..."
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This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertaining empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains are one of the most ubiquitous (macroscopic) causal structures is underdetermined by empirical data. It will be argued that even though the chain and its associated common cause model are empirically equivalent there exists an important asymmetry between the two models with respect to model expansions. This asymmetry might constitute a basis on which to disambiguate corresponding causal inferences on nonempirical grounds.
ORIGINAL ARTICLE The Causal Chain Problem
"... Abstract This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertinent empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic ..."
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Abstract This paper addresses a problem that arises when it comes to inferring deterministic causal chains from pertinent empirical data. It will be shown that to every deterministic chain there exists an empirically equivalent common cause structure. Thus, our overall conviction that deterministic chains are one of the most ubiquitous (macroscopic) causal structures is underdetermined by empirical data. It will be argued that even though the chain and its associated common cause model are empirically equivalent there exists an important asymmetry between the two models with respect to model expansions. This asymmetry might constitute a basis on which to disambiguate corresponding causal inferences on nonempirical grounds.
EJPS manuscript No. (will be inserted by the editor) Identifying intervention variables
"... The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as socalled intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal disc ..."
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The essential precondition of implementing interventionist techniques of causal reasoning is that particular variables are identified as socalled intervention variables. While the pertinent literature standardly brackets the question how this can be accomplished in concrete contexts of causal discovery, the first part of this paper shows that the interventionist nature of variables cannot, in principle, be established based only on an interventionist notion of causation. The second part then demonstrates that standard observational methods that draw on Bayesian networks identify intervention variables only if they also answer all the questions that can be answered by interventionist techniques—which are thus rendered dispensable. The paper concludes by suggesting a way of identifying intervention variables that allows for exploiting the whole inferential potential of interventionist techniques.