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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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Cited by 8 (1 self)
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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
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 6 (2 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
Statistics and Causality: Separated to Reunite Commentary on Bryan Dowd’s “Separated at Birth”
, 2010
"... Bryan Dowd (2010) should be commended for laying before us the historical roots of the tensions between statisticians and econometricians which, until today, perpetuate the myth that causal inference is somehow confusing, enigmatic or controversial. While modern analysis has proven this myth baseles ..."
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Bryan Dowd (2010) should be commended for laying before us the historical roots of the tensions between statisticians and econometricians which, until today, perpetuate the myth that causal inference is somehow confusing, enigmatic or controversial. While modern analysis has proven this myth baseless, it is often the historical accounts that put things in the
Health Services Research r Health Research and Educational Trust DOI: 10.1111/j.14756773.2011.01243.x COMMENTARY Statistics and Causality: Separated to
"... Bryan Dowd (2010) should be commended for laying before us the historical roots of the tensions between statisticians and econometricians which, until today, perpetuate the myth that causal inference is somehow confusing, enigmatic, or controversial. While modern analysis has proven this ..."
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Bryan Dowd (2010) should be commended for laying before us the historical roots of the tensions between statisticians and econometricians which, until today, perpetuate the myth that causal inference is somehow confusing, enigmatic, or controversial. While modern analysis has proven this
On Measurement Bias in Causal Inference Judea Pearl
, 2010
"... This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametr ..."
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This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining biasfree effect estimates in such models. cannot be estimated without bias. It turns out, however, that if we are given the conditional probabilities P (wz) that govern the error mechanism we can perform a modifiedadjustment for W that, in the limit of a very large sample, would amount to the same thing as observing and adjusting for Z itself, thus rendering the causal effect identifiable. Z P ( w  z)
EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION
, 2012
"... Mulaik, Johannes Textor, and other researchers from SEMNET for their comments on and critiques of our paper. Bollen’s work was partially supported by NSF SES 0617276. 1 EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION MODELS Social scientists ’ interest in causal effects is as old as the social s ..."
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Mulaik, Johannes Textor, and other researchers from SEMNET for their comments on and critiques of our paper. Bollen’s work was partially supported by NSF SES 0617276. 1 EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION MODELS Social scientists ’ interest in causal effects is as old as the social sciences. Attention to the philosophical underpinnings and the methodological challenges of analyzing causality has waxed and waned. Other authors in this volume trace the history of the concept of causality in the social sciences and we leave this task to their skilled hands. But we do note that we are at a time when there is a renaissance, if not a revolution in the methodology of causal inference, and structural equation models play a major role in this renaissance. Our emphasis in this chapter is on causality and structural equation models (SEMs). If nothing else, the pervasiveness of SEMs justifies such a focus. SEM applications are published in numerous substantive journals. Methodological developments on SEMs regularly appear in journals such as Sociological Methods & Research, Psychometrika, Sociological Methodology, Multivariate Behavioral Research, Psychological Methods,
On Measurement Bias in Causal Inference Judea Pearl
"... This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametr ..."
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This paper addresses the problem of measurement errors in causal inference and highlights several algebraic and graphical methods for eliminating systematic bias induced by such errors. In particulars, the paper discusses the control of partially observable confounders in parametric and non parametric models and the computational problem of obtaining biasfree effect estimates in such models. cannot be estimated without bias. It turns out, however, that if we are given the conditional probabilities P(wz) that govern the error mechanism we can perform a modifiedadjustment for W that, in the limit of a very large sample, would amount to the same thing as observing and adjusting for Z itself, thus rendering the causal effect identifiable. Z P ( w  z)
Chapter 13 Graphical Causal Models
"... Abstract This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs ’ main uses, discusses central principles, and gives applied examples. DAGs are visual representations of qualitative causal assumptions: They encod ..."
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Abstract This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs ’ main uses, discusses central principles, and gives applied examples. DAGs are visual representations of qualitative causal assumptions: They encode researchers ’ beliefs about how the world works. Straightforward rules map these causal assumptions onto the associations and independencies in observable data. The two primary uses of DAGs are (1) determining the identifiability of causal effects from observed data and (2) deriving the testable implications of a causal model. Concepts covered in this chapter include identification, dseparation, confounding, endogenous selection, and overcontrol. Illustrative applications then demonstrate that conditioning on variables at any stage in a causal process can induce as well as remove bias, that confounding is a fundamentally causal rather than anassociationalconcept,thatconventional approaches to causal mediation analysis are often biased, and that causal inference in social networks inherently faces endogenous selection bias. The chapter discusses several graphical criteria for the identification of causal effects of single, timepoint treatments (including the famous backdoor criterion), as well identification criteria for multiple, timevarying treatments.