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51
Causal inference in statistics: An Overview
, 2009
"... This review presents empirical researcherswith recent advances in causal inference, and 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 ca ..."
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Cited by 61 (12 self)
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This review presents empirical researcherswith recent advances in causal inference, and 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, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, 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 (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects ” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret, ” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potentialoutcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both.
Identification of Social Interactions
, 2010
"... While interest in social determinants of individual behavior has led to a rich theoretical literature and many efforts to measure these influences, a mature “social econometrics ” has yet to emerge. This chapter provides a critical overview of the identification of social interactions. We consider l ..."
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Cited by 28 (3 self)
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While interest in social determinants of individual behavior has led to a rich theoretical literature and many efforts to measure these influences, a mature “social econometrics ” has yet to emerge. This chapter provides a critical overview of the identification of social interactions. We consider linear and discrete choice models as well as social networks structures. We also consider experimental and quasiexperimental methods. In addition to describing the state of the identification literature, we indicate areas where additional research is especially needed and suggest some directions that appear to be especially promising.
2012): "What Linear Estimators Miss: The Effects of Family Income on Child Outcomes
 American Economic Journal: Applied Economics
"... This paper examines the causal relationship between family income and child outcomes. Motivated by theoretical predictions and OLS results suggesting a nonlinear relationship, we depart from previous studies in allowing the marginal effects on children’s outcomes of an increase in family income to ..."
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Cited by 21 (5 self)
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This paper examines the causal relationship between family income and child outcomes. Motivated by theoretical predictions and OLS results suggesting a nonlinear relationship, we depart from previous studies in allowing the marginal effects on children’s outcomes of an increase in family income to vary across the income distribution. Our nonlinear IV and fixedeffect estimates show an increasing, concave relationship between family income and children’s outcomes. By decomposing the linear estimators, we show that the linear estimates miss the effects of family income, because they assign little weight to the large marginal effects in the lower part of the income distribution.
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 15 (5 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 Impact of ThinCapitalization Rules on External Debt Usage – A Propensity Score Matching Approach
, 2008
"... This paper analyzes how multinational enterprises respond to a restriction on interest deductions incurred for internal borrowing. The emphasis of the study is on a firm’s response with respect to external borrowing. The empirical investigation applies propensity score matching techniques and exploi ..."
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Cited by 13 (0 self)
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This paper analyzes how multinational enterprises respond to a restriction on interest deductions incurred for internal borrowing. The emphasis of the study is on a firm’s response with respect to external borrowing. The empirical investigation applies propensity score matching techniques and exploits the 2001 reform of the German thincapitalization rule to solve endogeneity problems. The results suggest that restrictions on internal debt are associated with expansions in external debt finance, indicating a substitutional relationship. Since multinational enterprises can use internal debt to shift profits from high to lowtax countries, this finding implies that policies aimed at securing corporate tax revenue possibly fail and should be subject to careful scrutiny by policymakers. JEL Code: G32, H25.
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 11 (4 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
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 10 (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.
Assessing the evidence on neighborhood effects from Moving to Opportunity. Federal Reserve Bank of Cleveland Working Paper
, 2012
"... w o r k i n g ..."
What linear estimators miss: Reexamining the effects of family income on child outcomes. Working Paper
, 2010
"... This paper uses a rich Norwegian dataset to reexamine the causal relationship between family income and child outcomes. Motivated by theoretical predictions and OLS results that suggest a nonlinear relationship, we depart from previous studies in allowing the marginal effects on children’s outcomes ..."
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Cited by 8 (1 self)
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This paper uses a rich Norwegian dataset to reexamine the causal relationship between family income and child outcomes. Motivated by theoretical predictions and OLS results that suggest a nonlinear relationship, we depart from previous studies in allowing the marginal effects on children’s outcomes of an increase in family income to vary across the income distribution. Our nonlinear IV and fixedeffect estimates show an increasing, concave relationship between family income and children’s educational attainment and IQ. The linear estimates, however, suggest small, if any, effect of family income, because they assign little weight to the large marginal effects at the lower part of the income distribution.