scholarly journals "Toward a Clearer Definition of Confounding" Revisited With Directed Acyclic Graphs

2012 ◽  
Vol 176 (6) ◽  
pp. 506-511 ◽  
Author(s):  
P. P. Howards ◽  
E. F. Schisterman ◽  
C. Poole ◽  
J. S. Kaufman ◽  
C. R. Weinberg
Keyword(s):  
Directed Acyclic Graphs ◽  
Acyclic Graphs ◽  
Definition Of

scholarly journals CAUSAL ANALYSIS AFTER HAAVELMO

2014 ◽  
Vol 31 (1) ◽  
pp. 115-151 ◽  
Author(s):  
James Heckman ◽  
Rodrigo Pinto
Keyword(s):  
Economic Models ◽  
Causal Analysis ◽  
Directed Acyclic Graphs ◽  
Ceteris Paribus ◽  
Acyclic Graphs ◽  
Rigorous Treatment ◽  
Definition Of

Haavelmo’s seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined usinghypotheticalmodels that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall’s (1890)ceteris paribusanalysis. We embed Haavelmo’s framework into the recursive framework of Directed Acyclic Graphs (DAGs) commonly used in the literature of causality (Pearl, 2000) and Bayesian nets (Lauritzen, 1996). We compare the analysis of causality based on a methodology inspired by Haavelmo’s ideas with other approaches used in the causal literature of DAGs. We discuss the limitations of methods that solely use the information expressed in DAGs for the identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo.

Modeling overlapping structures

2013 ◽  
Author(s):  
Yves Marcoux ◽  
Michael Sperberg-McQueen ◽  
Claus Huitfeldt
Keyword(s):  
Directed Graphs ◽  
Upper Bounds ◽  
Directed Acyclic Graphs ◽  
Markup Language ◽  
Multiple Roots ◽  
Acyclic Graph ◽  
Xml Documents ◽  
Acyclic Graphs ◽  
Structured Document ◽  
Definition Of

The problem of overlapping structures has long been familiar to the structured document community. In a poem, for example, the verse and line structures overlap, and having them both available simultaneously is convenient, and sometimes necessary (for example for automatic analyses). However, only structures that embed nicely can be represented directly in XML. Proposals to address this problem include XML solutions (based essentially on a layer of semantics) and non-XML ones. Among the latter is TexMecs HS2003, a markup language that allows overlap (and many other features). XML documents, when viewed as graphs, correspond to trees. Marcoux M2008 characterized overlap-only TexMecs documents by showing that they correspond exactly to completion-acyclic node-ordered directed acyclic graphs. In this paper, we elaborate on that result in two ways. First, we cast it in the setting of a strictly larger class of graphs, child-arc-ordered directed graphs, that includes multi-graphs and non-acyclic graphs, and show that — somewhat surprisingly — it does not hold in general for graphs with multiple roots. Second, we formulate a stronger condition, full-completion-acyclicity, that guarantees correspondence with an overlap-only document, even for graphs that have multiple roots. The definition of fully-completion-acyclic graph does not in itself suggest an efficient algorithm for checking the condition, nor for computing a corresponding overlap-only document when the condition is satisfied. We present basic polynomial-time upper bounds on the complexity of accomplishing those tasks.

scholarly journals Decision-Theoretic Foundations for Causal Reasoning

1995 ◽  
Vol 3 ◽  
pp. 405-430 ◽  
Author(s):  
D. Heckerman ◽  
R. Shachter
Keyword(s):  
Canonical Form ◽  
Special Class ◽  
Causal Reasoning ◽  
Directed Acyclic Graphs ◽  
Influence Diagrams ◽  
Counterfactual Reasoning ◽  
Cause And Effect ◽  
Acyclic Graphs ◽  
Definition Of ◽  
Theoretic Foundations

We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning.

scholarly journals Oropharyngeal Dysphagia: A Proposal for an Ecological Theoretical Model

2022 ◽  
pp. 7-16
Author(s):  
Rafaela Soares Rech ◽  
Bárbara Niegia Garcia de Goulart
Keyword(s):  
Data Analysis ◽  
Theoretical Model ◽  
Oropharyngeal Dysphagia ◽  
Theoretical Models ◽  
Epidemiological Studies ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs ◽  
Confounding Variables ◽  
Direct Causation ◽  
Definition Of

Background: The exponential growth in epidemiological studies has been reflected in an increase in analytical studies. Thus, theoretical models are required to guide the definition of data analysis, although so far, they are seldom used in Speech, Language, and Hearing Sciences. Objective: To propose a multicausal model for oropharyngeal dysphagia using directed acyclic graphs showing mediating variables, confounding variables, and variables connected by direct causation. Design: This integrative literature review. Setting: This was carried out until January 4, 2021, and searches were performed with the MEDLINE, EMBASE,and other bases.

scholarly journals An Introduction to Causal Inference

2020 ◽  
Author(s):  
Fabian Dablander
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Directed Acyclic Graphs ◽  
Causal Models ◽  
Causal Relationships ◽  
Counterfactual Reasoning ◽  
Graphical Approach ◽  
Acyclic Graphs ◽  
Definition Of ◽  
Simpson's Paradox

Causal inference goes beyond prediction by modeling the outcome of interventions and formalizing counterfactual reasoning. Instead of restricting causal conclusions to experiments, causal inference explicates the conditions under which it is possible to draw causal conclusions even from observational data. In this paper, I provide a concise introduction to the graphical approach to causal inference, which uses Directed Acyclic Graphs (DAGs) to visualize, and Structural Causal Models (SCMs) to relate probabilistic and causal relationships. Successively, we climb what Judea Pearl calls the "causal hierarchy" --- moving from association to intervention to counterfactuals. I explain how DAGs can help us reason about associations between variables as well as interventions; how the do-calculus leads to a satisfactory definition of confounding, thereby clarifying, among other things, Simpson's paradox; and how SCMs enable us to reason about what could have been. Lastly, I discuss a number of challenges in applying causal inference in practice.

scholarly journals Directed acyclic graphs: An under-utilized tool for child maltreatment research

2019 ◽  
Vol 91 ◽  
pp. 78-87 ◽  
Author(s):  
Anna E. Austin ◽  
Tania A. Desrosiers ◽  
Meghan E. Shanahan
Keyword(s):  
Child Maltreatment ◽  
Directed Acyclic Graphs ◽  
Acyclic Graphs
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