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23
The Art of Causal Conjecture
, 1996
"... Causal relations are regularities in the way Nature’s predictions change. Since we usually do not stand in Nature’s shoes, we usually do not observe these dynamic regularities directly. But we sometimes observe statistical regularities that are most easily explained by hypothesizing such dynamic reg ..."
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Cited by 84 (18 self)
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Causal relations are regularities in the way Nature’s predictions change. Since we usually do not stand in Nature’s shoes, we usually do not observe these dynamic regularities directly. But we sometimes observe statistical regularities that are most easily explained by hypothesizing such dynamic regularities. In this chapter, I illustrate this process of causal conjecture with a few simple examples. I first consider a negative causal relation: causal uncorrelatedness. Two variables are causally uncorrelated if there are no steps in Nature’s event tree that change them both in expected value. They have, in this sense, no common causes. This implies, as we shall see, that the two variables are uncorrelated in the classical sense in every situation in the tree. When we observe that variables are uncorrelated in many different situations, then we may conjecture that this is due to their being causally uncorrelated. I will also discuss three causal relations of a positive character. These relations assert, each in a different way, that the causes (steps in Nature’s tree) that affect a certain variable X also affect another variable Y. This implies regularities in certain classical statistical predictions. The first causal relation, which I call linear sign, implies regularity in linear regression. The second, scored sign, implies regularity in conditional
An Axiomatic Characterization of Causal Counterfactuals
, 1998
"... This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedback ..."
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Cited by 44 (17 self)
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This paper studies the causal interpretation of counterfactual sentences using a modifiable structural equation model. It is shown that two properties of counterfactuals, namely, composition and effectiveness, are sound and complete relative to this interpretation, when recursive (i.e., feedbackless) models are considered. Composition and effectiveness also hold in Lewis's closestworld semantics, which implies that for recursive models the causal interpretation imposes no restrictions beyond those embodied in Lewis's framework. A third property, called reversibility, holds in nonrecursive causal models but not in Lewis's closestworld semantics, which implies that Lewis's axioms do not capture some properties of systems with feedback. Causal inferences based on counterfactual analysis are exemplified and compared to those based on graphical models.
Graphs, Causality, And Structural Equation Models
, 1998
"... Structural equation modeling (SEM) has dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many SEM practitioners are having difficulty articulating the causal content of SEM and are seeking foundational answers. ..."
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Cited by 44 (14 self)
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Structural equation modeling (SEM) has dominated causal analysis in the social and behavioral sciences since the 1960s. Currently, many SEM practitioners are having difficulty articulating the causal content of SEM and are seeking foundational answers.
THE SCIENTIFIC MODEL OF CAUSALITY
, 2005
"... Causality is a very intuitive notion that is difficult to make precise without lapsing into tautology. Two ingredients are central to any definition: (1) a set of possible outcomes (counterfactuals) generated by a function of a set of ‘‘factors’ ’ or ‘‘determinants’ ’ and (2) a manipulation where on ..."
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Cited by 20 (1 self)
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Causality is a very intuitive notion that is difficult to make precise without lapsing into tautology. Two ingredients are central to any definition: (1) a set of possible outcomes (counterfactuals) generated by a function of a set of ‘‘factors’ ’ or ‘‘determinants’ ’ and (2) a manipulation where one (or more) of the ‘‘factors’ ’ or ‘‘determinants’’ is changed. An effect is realized as a change in the argument of a stable function that produces the same change in the outcome for a class of interventions that change the ‘‘factors’ ’ by the same amount. The outcomes are compared at different levels of the factors or generating variables. Holding all factors save one at a constant level, the change in the outcome associated with manipulation of the varied factor is called a causal effect of the manipulated factor. This definition, or some version of it, goes back to Mill (1848) and Marshall (1890). Haavelmo’s (1943) made it more precise within the context of linear equations models. The phrase ‘ceteris paribus’ (everything else held constant) is a mainstay of economic analysis
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 16 (6 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.
Why There Is No Statistical Test For Confounding, Why Many Think There Is, And Why They Are Almost Right
, 1998
"... this paper is to bring to the attention of investigators several basic limitations of the associational criterion. We will show that the associational criterion does not ensure unbiased e#ect estimates, nor does it follow from the requirement of unbiasedness. After demonstrating, by examples, the ab ..."
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Cited by 12 (4 self)
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this paper is to bring to the attention of investigators several basic limitations of the associational criterion. We will show that the associational criterion does not ensure unbiased e#ect estimates, nor does it follow from the requirement of unbiasedness. After demonstrating, by examples, the absence of logical connections between the statistical and the causal notions of confounding, we will de#ne a stronger notion of unbiasedness, called stable unbiasedness, relative to which a modi#ed statistical criterion will be shown necessary and su#cient. The necessary part will then yield a practical test for stable unbiasedness which, remarkably, does not require knowledge of all potential confounders in a problem. Finally,wewill argue that the prevailing practice of substituting statistical criteria for the e#ectbased de#nition of confounding is not entirely misguided, because stable unbiasedness is in fact what investigators have been and should be aiming to achieve, and stable unbiasedness is what statistical criteria can test.
Tantalus on the Road to Asymptopia
"... good case for relying on purposefully randomized or accidentally randomized experiments to relieve the doubts that afflict inferences from nonexperimental data. On further reflection, I realized that I may have been overcome with irrational exuberance. Moreover, with this great honor bestowed on my ..."
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Cited by 12 (0 self)
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good case for relying on purposefully randomized or accidentally randomized experiments to relieve the doubts that afflict inferences from nonexperimental data. On further reflection, I realized that I may have been overcome with irrational exuberance. Moreover, with this great honor bestowed on my “con ” article, I couldn’t easily throw this child of mine overboard. We economists trudge relentlessly toward Asymptopia, where data are unlimited and estimates are consistent, where the laws of large numbers apply perfectly and where the full intricacies of the economy are completely revealed. But it’s a frustrating journey, since, no matter how far we travel, Asymptopia remains infinitely far away. Worst of all, when we feel pumped up with our progress, a tectonic shift can occur, like the Panic of 2008, making it seem as though our long journey has left us disappointingly close to the State of Complete Ignorance whence we began. The pointlessness of much of our daily activity makes us receptive when the Priests of our tribe ring the bells and announce a shortened path to Asymptopia. (Remember the Cowles Foundation offering asymptotic properties of simultaneous equations estimates
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 ..."
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Cited by 11 (1 self)
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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.
The Effect of DeficitReduction Laws on Real Interest Rates,’ Discussion paper
 Board of Governors of the Federal Reserve System Finance and Economics Discussion Series: 96/44, Board of Governors of the Federal Reserve System Working Paper
, 1996
"... valuable suggestions. The views expressed in this paper are not necessarily those of the Federal The periods preceding the passage of the GrammRudmanHollings law of 1985 and the Budget Enforcement Act of 1990 form excellent natural experiments for studying the effect of fiscal policy on financial ..."
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Cited by 5 (0 self)
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valuable suggestions. The views expressed in this paper are not necessarily those of the Federal The periods preceding the passage of the GrammRudmanHollings law of 1985 and the Budget Enforcement Act of 1990 form excellent natural experiments for studying the effect of fiscal policy on financial markets. Financial markets should respond to expected changes in government spending and budget deficits, but those expectations are generally unobservable. This paper uses news reports about these two deficitreduction laws to identify days when expected fiscal policy clearly became more or less expansionary. The paper also proposes a technique for identifying whether the real interest rate increased or decreased on those days, based on changes in the nominal interest rate, the exchange rate, commodity prices, and stock prices. The financialmarket developments following news reports about the deficitreduction laws are consistent with the predictions of economic theory. Higher expected government spending and budget deficits raised real interest rates and the value of the dollar, while lower Ricardian theory predicts that temporary reductions in government spending should lower
Vector autoregressions, policy analysis, and directed acyclic graphs: an application to the US
 Economy, Journal of Applied Economics
, 2003
"... The paper considers the use of directed acyclic graphs (DAGs), and their construction from observational data with PCalgorithm TETRAD II, in providing overidentifying restrictions on the innovations from a vector autoregression. Results from Sims ’ 1986 model of the US economy are replicated and c ..."
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Cited by 4 (2 self)
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The paper considers the use of directed acyclic graphs (DAGs), and their construction from observational data with PCalgorithm TETRAD II, in providing overidentifying restrictions on the innovations from a vector autoregression. Results from Sims ’ 1986 model of the US economy are replicated and compared using these datadriven techniques. The directed graph results show Sims ’ sixvariable VAR is not rich enough to provide an unambiguous ordering at usual levels of statistical significance. A significance level in the neighborhood of 30 % is required to find a clear structural ordering. Although the DAG results are in agreement with Sims ’ theorybased model for unemployment, differences are noted for the other five variables: income, money supply, price level, interest rates, and investment. Overall the DAG results are broadly consistent with a monetarist view with adaptive expectations and no hyperinflation.