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An Extended Class of Instrumental Variables for the Estimation of Causal Effects
 UCSD DEPT. OF ECONOMICS DISCUSSION PAPER
, 1996
"... This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regresso ..."
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Cited by 32 (11 self)
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This paper builds on the structural equations, treatment effect, and machine learning literatures to provide a causal framework that permits the identification and estimation of causal effects from observational studies. We begin by providing a causal interpretation for standard exogenous regressors and standard “valid” and “relevant” instrumental variables. We then build on this interpretation to characterize extended instrumental variables (EIV) methods, that is methods that make use of variables that need not be valid instruments in the standard sense, but that are nevertheless instrumental in the recovery of causal effects of interest. After examining special cases of single and double EIV methods, we provide necessary and sufficient conditions for the identification of causal effects by means of EIV and provide consistent and asymptotically normal estimators for the effects of interest.
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 11 (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.
Settable Systems: An Extension of Pearl’s Causal Model with Optimization, Equilibrium, and Learning
, 2008
"... Judea Pearl’s Causal Model is a rich framework that provides deep insight into the nature of causal relations. As yet, however, the Pearl Causal Model (PCM) has not had much impact on economics or econometrics. This may be due in part to the fact that the PCM is not as well suited to analyzing econo ..."
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Cited by 10 (5 self)
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Judea Pearl’s Causal Model is a rich framework that provides deep insight into the nature of causal relations. As yet, however, the Pearl Causal Model (PCM) has not had much impact on economics or econometrics. This may be due in part to the fact that the PCM is not as well suited to analyzing economic structures as might be desired. We o¤er the settable systems framework as an extension of the PCM that embodies features of central interest to economists and econometricians: optimization, equilibrium, and learning. Because these are common features of physical, natural, or social systems, our framework may prove generally useful. In particular, settable systems o¤er a number of advantages relative to the PCM for machine learning. Important distinguishing features of the settable systems framework are its countable dimensionality, its treatment of attributes, the absence of a …xedpoint requirement, and the use of partitioning and partitionspeci…c response functions to accommodate the behavior of optimizing and interacting agents. A series of closely related machine learning examples and examples from game theory and machine learning with feedback demonstrates limitations of the PCM and motivates the distinguishing features of settable systems.
Caveats for Causal Reasoning with Equilibrium Models
, 2003
"... Abstract. In this paper 1 we examine the ability to perform causal reasoning with recursive equilibrium models. We identify a critical postulate, which we term the Manipulation Postulate, that is required in order to perform causal inference, and we prove that there exists a general class F of recur ..."
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Cited by 6 (2 self)
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Abstract. In this paper 1 we examine the ability to perform causal reasoning with recursive equilibrium models. We identify a critical postulate, which we term the Manipulation Postulate, that is required in order to perform causal inference, and we prove that there exists a general class F of recursive equilibrium models that violate the Manipulation Postulate. We relate this class to the existing phenomenon of reversibility and show that all models in F display reversible behavior, thereby providing an explanation for reversibility and suggesting that it is a special case of a more general and perhaps widespread problem. We also show that all models in F possess a set of variables V ′ whose manipulation will cause an instability such that no equilibrium model will exist for the system. We define the Structural Stability Principle which provides a graphical criterion for stability in causal models. Our theorems suggest that drastically incorrect inferences may be obtained when applying the Manipulation Postulate to equilibrium models, a result which has implications for current work on causal modeling, especially causal discovery from data. 1
Granger Causality and Dynamic Structural Systems
, 2008
"... We analyze the relations between Granger (G) noncausality and a notion of structural causality arising naturally from a general nonseparable recursive dynamic structural system. Building on classical notions of G noncausality, we introduce interesting and natural extensions, namely weak G noncaus ..."
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Cited by 6 (2 self)
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We analyze the relations between Granger (G) noncausality and a notion of structural causality arising naturally from a general nonseparable recursive dynamic structural system. Building on classical notions of G noncausality, we introduce interesting and natural extensions, namely weak G noncausality and retrospective weak G noncausality. We show that structural noncausality and certain (retrospective) conditional exogeneity conditions imply (retrospective) (weak) G noncausality. We strengthen structural causality to notions of (retrospective) strong causality and show that (retrospective) strong causality implies (retrospective) weak G causality. We provide practical conditions and straightforward new methods for testing (retrospective) weak G noncausality, (retrospective) conditional exogeneity, and structural noncausality. Finally, we apply our methods to explore structural causality in industrial pricing, macroeconomics, and …nance.
Parametric and Nonparametric Estimation of CovariateConditioned Average Effects
 UCSD DEPT. OF ECONOMICS DISCUSSION PAPER
, 2005
"... This paper unifies three complementary approaches to defining, identifying, and estimating causal effects: the classical structural equations approach of the Cowles Commision; the treatment effects framework of Rubin (1974) and Rosenbaum and Rubin (1983); and the Directed Acyclic Graph (DAG) approac ..."
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Cited by 4 (4 self)
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This paper unifies three complementary approaches to defining, identifying, and estimating causal effects: the classical structural equations approach of the Cowles Commision; the treatment effects framework of Rubin (1974) and Rosenbaum and Rubin (1983); and the Directed Acyclic Graph (DAG) approach of Pearl. The settable system framework nests these prior approaches, while affording significant improvements to each. For example, the settable system approach permits identification and estimation of causal effects without requiring exogenous instruments, generalizing the classical structural equations approach; it relaxes the stable unit treatment value assumption of the treatment effect approach and provides significant insight into the selection of covariates; and it accommodates mutual causality, generalizing the DAG approach. We provide necessary and sufficient conditions for identification of covariateconditioned average causal effects, parametric and nonparametric estimation results, and new tests for unconfoundedness.
A Unified Framework for Defining and Identifying Causal Effects
, 2006
"... This paper unifies three complementary approaches to defining, identifying, and estimating causal effects: the classical structural equations approach of the Cowles Commision; the treatment effects framework of Rubin (1974) and Rosenbaum and Rubin (1983); and the Directed Acyclic Graph (DAG) appro ..."
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Cited by 4 (0 self)
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This paper unifies three complementary approaches to defining, identifying, and estimating causal effects: the classical structural equations approach of the Cowles Commision; the treatment effects framework of Rubin (1974) and Rosenbaum and Rubin (1983); and the Directed Acyclic Graph (DAG) approach of Pearl. The settable system framework nests these prior approaches, while affording significant improvements to each. For example, the settable system approach permits identification of causal effects without requiring exogenous instruments; instead, a weaker conditional exogeneity condition suffices. It removes the stable unit treatment value assumption of the treatment effect approach and provides significant insight into the selection of covariates. It generalizes the DAG approach by accommodating mutual causality and attributes. We provide a variety of results ensuring structural identification of general covariateconditioned average causal effects, laying the foundation for parametric and nonparametric estimation of effects of interest and new tests for structural identification.
DISCUSSION: ‘THE SCIENTIFIC MODEL OF CAUSALITY’
"... Heckman advocates an approach to causal inference that draws upon structural modeling of the outcome(s) of interest (which he calls scientific), and he contrasts this approach sharply with that arising out of the statistical literature on experimentation. Drawing extensively ..."
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Cited by 2 (0 self)
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Heckman advocates an approach to causal inference that draws upon structural modeling of the outcome(s) of interest (which he calls scientific), and he contrasts this approach sharply with that arising out of the statistical literature on experimentation. Drawing extensively
Retrospective Estimation of Causal Effects Through Time
 A FESTSCHRIFT IN HONOUR OF DAVID HENDRY
, 2009
"... This paper provides methods for estimating a variety of retrospective measures of causal effects in systems of dynamic structural equations. These equations need not be linear or separable. Structural identification of effects of interest is ensured by certain conditional exogeneity conditions, an ..."
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Cited by 2 (2 self)
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This paper provides methods for estimating a variety of retrospective measures of causal effects in systems of dynamic structural equations. These equations need not be linear or separable. Structural identification of effects of interest is ensured by certain conditional exogeneity conditions, an extension of the notion of strict exogeneity. The covariates ensuring conditional exogeneity can contain not only lags but also leads of suitable proxies for unobservables. We focus on covariateconditioned average and quantile effects, together with counterfactual objects that are associated with these, such as point bands and path bands. The latter are useful for constructing confidence intervals and testing hypotheses. We show how these objects can be estimated using statespace methods and illustrate with a study of the impact of crude oil prices on gasoline prices.
Automated search for causal relations: Theory and practice
 Heuristics, Probability and Causality: A Tribute to Judea Pearl
, 2010
"... The rapid spread of interest in the last two decades in principled methods of search or estimation of causal relations has been driven in part by technological developments, especially the changing nature of modern data collection and storage techniques, and the increases in the speed and storage ca ..."
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Cited by 2 (0 self)
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The rapid spread of interest in the last two decades in principled methods of search or estimation of causal relations has been driven in part by technological developments, especially the changing nature of modern data collection and storage techniques, and the increases in the speed and storage capacities of computers. Statistics books from 30 years