Results 1  10
of
118
A general identification condition for causal effects
 In Eighteenth National Conference on Artificial Intelligence
"... This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called “causal graph”, in which some variables are presume ..."
Abstract

Cited by 57 (18 self)
 Add to MetaCart
This paper concerns the assessment of the effects of actions or policy interventions from a combination of: (i) nonexperimental data, and (ii) substantive assumptions. The assumptions are encoded in the form of a directed acyclic graph, also called “causal graph”, in which some variables are presumed to be unobserved. The paper establishes a necessary and sufficient criterion for the identifiability of the causal effects of a singleton variable on all other variables in the model, and apowerful sufficient criterion for the effects of a singleton variable on any set of variables.
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. ..."
Abstract

Cited by 44 (14 self)
 Add to MetaCart
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.
Marginal structural models and causal inference in epidemiology
 Epidemiology
, 2000
"... In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist timedependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal mo ..."
Abstract

Cited by 35 (1 self)
 Add to MetaCart
In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist timedependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverseprobabilityoftreatment weighted estimators. (Epidemiology 2000;11:550–560)
From association to causation: Some remarks on the history of statistics
 Statist. Sci
, 1999
"... The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More ..."
Abstract

Cited by 23 (6 self)
 Add to MetaCart
The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More recently, investigators in the social and life sciences have used statistical models and significance tests to deduce causeandeffect relationships from patterns of association; an early example is Yule’s study on the causes of poverty (1899). In my view, this modeling enterprise has not been successful. Investigators tend to neglect the difficulties in establishing causal relations, and the mathematical complexities 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. Snow’s work on cholera will be contrasted with modern studies that depend on statistical models and tests of significance. The examples may help to clarify the limits of current statistical techniques for making causal inferences from patterns of association. 1.
Causal diagrams
, 2008
"... Abstract: From their inception, causal systems models (more commonly known as structuralequations models) have been accompanied by graphical representations or path diagrams that provide compact summaries of qualitative assumptions made by the models. These diagrams can be reinterpreted as probabil ..."
Abstract

Cited by 23 (2 self)
 Add to MetaCart
Abstract: From their inception, causal systems models (more commonly known as structuralequations models) have been accompanied by graphical representations or path diagrams that provide compact summaries of qualitative assumptions made by the models. These diagrams can be reinterpreted as probability models, enabling use of graph theory in probabilistic inference, and allowing easy deduction of independence conditions implied by the assumptions. They can also be used as a formal tool for causal inference, such as predicting the effects of external interventions. Given that the diagram is correct, one can see whether the causal effects of interest (target effects, or causal estimands) can be estimated from available data, or what additional observations are needed to validly estimate those effects. One can also see how to represent the effects as familiar standardized effect measures. The present article gives an overview of: (1) components of causal graph theory; (2) probability interpretations of graphical models; and (3) methodologic implications of the causal and probability structures encoded in the graph, such as sources of bias and the data needed for their control.
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 ..."
Abstract

Cited by 23 (8 self)
 Add to MetaCart
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.
Data, design and background knowledge in etiologic inference. Epidemiology
, 2001
"... I use two examples to demonstrate that an appropriate etiologic analysis of an epidemiologic study depends as much on study design and background subjectmatter knowledge as on the data. The demonstration is facilitated by the use of causal graphs. (Epidemiology 2001;11:313–320) Key Words: inference ..."
Abstract

Cited by 19 (0 self)
 Add to MetaCart
I use two examples to demonstrate that an appropriate etiologic analysis of an epidemiologic study depends as much on study design and background subjectmatter knowledge as on the data. The demonstration is facilitated by the use of causal graphs. (Epidemiology 2001;11:313–320) Key Words: inference, etiology, study design, data collection, data analysis, epidemiologic methods Greenland et al 1 discussed the use of causal graphs in epidemiologic research. A limitation of that paper was that it was lacking concrete examples designed to help the reader see how to take one’s knowledge of study design, temporal ordering, basic biology, and epidemiologic principles to construct an appropriate causal graph. Here I present two epidemiologic thought experiments that make the point that the choice of an appropriate etiologic analysis depends as much on the design of the study and background subjectmatter knowledge as on the data. Specifically, in the first, I provide a single hypothetical dataset and three differing study designs, each of which plausibly could have given rise to the data. I show that the appropriate etiologic analysis differs with the design. In the second, I revisit a wellknown epidemiologic controversy from the late 1970s. Horowitz and Feinstein 2 proposed that the strong association between postmenopausal estrogens and endometrial cancer seen in many epidemiologic studies might be wholly attributable to diagnostic bias. Others disagreed. 3–5 Part of the discussion centered on the issue of whether it was appropriate to stratify on vaginal bleeding, the purported cause of the diagnostic bias in the analysis. The goal here is to show, using causal graphs, that the answer depends on underlying assumptions about the relevant biological mechanisms.
On specifying graphical models for causation, and the identification problem
 Evaluation Review
, 2004
"... This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs c ..."
Abstract

Cited by 18 (1 self)
 Add to MetaCart
This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
Four types of effect modification  a classification based on directed acyclic graphs
"... By expressing the conditional causal risk difference as a sum of products of stratum specific risk differences and conditional probabilities, it is possible to give a classification of the types of causal relationships that can give rise to effect modification on the risk difference scale. Directed ..."
Abstract

Cited by 18 (1 self)
 Add to MetaCart
By expressing the conditional causal risk difference as a sum of products of stratum specific risk differences and conditional probabilities, it is possible to give a classification of the types of causal relationships that can give rise to effect modification on the risk difference scale. Directed acyclic graphs make clear the necessary causal relationships for a particular variable to serve as an effect modifier for the causal risk diference concerning two other variables. The directed acyclic graph causal framework thereby gives rise to a fourfold classification for effect modi…cation: direct effect modification, indirect effect modification, effect modification by proxy and efect modification by a common cause. Brief discussion is given to the case of multiple effect modification relationships and multiple effect modifiers as well as measures of effect other than that of the causal risk difference.