Results 1  10
of
14
Healthy, Wealthy, and Wise? Tests for Direct Causal Paths
 Journal of Econometrics
, 2001
"... This paper utilizes the Asset and Health Dynamics of the Oldest Old (AHEAD) Panel to test for the absence of causal links from socioeconomic status (SES) to health innovations and mortality, and from health conditions to innovations in wealth. We conclude that there is no direct causal link from ..."
Abstract

Cited by 43 (2 self)
 Add to MetaCart
This paper utilizes the Asset and Health Dynamics of the Oldest Old (AHEAD) Panel to test for the absence of causal links from socioeconomic status (SES) to health innovations and mortality, and from health conditions to innovations in wealth. We conclude that there is no direct causal link from SES to mortality or to incidence of most sudden onset health conditions (accidents and some acute conditions), but there is an association of SES with incidence of gradual onset health conditions (mental conditions, and some degenerative and chronic conditions), due either to causal links or to persistent unobserved behavioral or genetic factors that have a common influence on both SES and innovations in health. We conclude that there is no direct causal link from health status to innovations in wealth.
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 ..."
Abstract

Cited by 21 (1 self)
 Add to MetaCart
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
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 ..."
Abstract

Cited by 12 (6 self)
 Add to MetaCart
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.
Estimating highdimensional intervention effects from observation data. The Ann
 of Stat
"... We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can ..."
Abstract

Cited by 8 (2 self)
 Add to MetaCart
We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can be estimated using intervention calculus. In this paper, we combine these two parts. For each DAG in the estimated equivalence class, we use intervention calculus to estimate the causal effects of the covariates on the response. This yields a collection of estimated causal effects for each covariate. We show that the distinct values in this set can be consistently estimated by an algorithm that uses only local information of the graph. This local approach is computationally fast and feasible in highdimensional problems. We propose to use summary measures of the set of possible causal effects to determine variable importance. In particular, we use the minimum absolute value of this set, since that is a lower bound on the size of the causal effect. We demonstrate the merits of our methods in a simulation study and on a data set about riboflavin production. 1. Introduction. Our
2010a Statistical inference after model selection
 Journal of Quantitative Criminology
"... Conventional statistical inference requires that a model of how the data were generated be known before the data are analyzed. Yet in criminology, and in the social sciences more broadly, a variety of model selection procedures are routinely undertaken followed by statistical tests and confidence in ..."
Abstract

Cited by 6 (5 self)
 Add to MetaCart
Conventional statistical inference requires that a model of how the data were generated be known before the data are analyzed. Yet in criminology, and in the social sciences more broadly, a variety of model selection procedures are routinely undertaken followed by statistical tests and confidence intervals computed for a “final ” model. In this paper, we examine such practices and show how they are typically misguided. The parameters being estimated are no longer well defined, and postmodelselection sampling distributions are mixtures
Causal Inference and the Heckman Model
"... In the social sciences, evaluating the effectiveness of a program or intervention often leads researchers to draw causal inferences from observational research designs. Bias in estimated causal effects becomes an obvious problem in such settings. This article presents the Heckman Model as an approac ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
In the social sciences, evaluating the effectiveness of a program or intervention often leads researchers to draw causal inferences from observational research designs. Bias in estimated causal effects becomes an obvious problem in such settings. This article presents the Heckman Model as an approach sometimes applied to observational data for the purpose of estimating an unbiased causal effect and shows how the Heckman Model can be used to correct for the problem of selection bias. It discusses in detail the assumptions necessary before the approach can be used to make causal inferences. The Heckman Model makes assumptions about the relationship between two equations in an underlying behavioral model: a response schedule and a selection function. This article shows that the Heckman Model is particularly sensitive to the choice of variables included in the selection function. This is demonstrated empirically in the context of estimating the effect of commercial coaching programs on the SAT performance of high school students. Coaching effects for both sections of the SAT are estimated using data from the National Education Longitudinal Study of 1988. Small changes in the selection function are shown to have a big impact on estimated coaching effects under the Heckman Model.
Statistical Models for Causation
, 2005
"... We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally probl ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally problematic. Therefore, causal relationships are seldom to be inferred from a data set by running statistical algorithms, unless there is substantial prior knowledge about the mechanisms that generated the data. We begin with linear models (regression analysis) and then turn to graphical models, which may in principle be nonlinear.
Statistical Models for Causation: A Critical Review
"... Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later ..."
Abstract
 Add to MetaCart
Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later
arXiv: 0810.4214 ESTIMATING HIGHDIMENSIONAL INTERVENTION EFFECTS FROM OBSERVATIONAL DATA
, 810
"... We assume that we have observational data, generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can ..."
Abstract
 Add to MetaCart
We assume that we have observational data, generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can be estimated using intervention calculus. In this paper, we combine these two parts. For each DAG in the estimated equivalence class, we use intervention calculus to estimate the causal effects of the covariates on the response. This yields a collection of estimated causal effects for each covariate. We show that the distinct values in this set can be consistently estimated by an algorithm that uses only local information of the graph. This local approach is computationally fast and feasible in highdimensional problems. We propose to use summary measures of the set of possible causal effects to determine variable importance. In particular, we use the minimum absolute value of this set, since that is a lower bound on the size of the causal effect. We demonstrate the merits of our methods in a simulation study, and on a data set about riboflavin production. 1. Introduction. Our