scholarly journals Causal Diagrams and Three Pairs of Biases

10.5772/33486 ◽  
2012 ◽  
Author(s):  
Eyal Shahar ◽  
Doron J.
Keyword(s):  
Causal Diagrams

scholarly journals Analysis of Body Mass Index and Mortality in Patients With Colorectal Cancer Using Causal Diagrams

JAMA Oncology ◽  
2016 ◽  
Vol 2 (9) ◽  
pp. 1137 ◽  
Author(s):  
Candyce H. Kroenke ◽  
Romain Neugebauer ◽  
Jeffrey Meyerhardt ◽  
Carla M. Prado ◽  
Erin Weltzien ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Body Mass Index ◽  
Body Mass ◽  
Causal Diagrams

Causal diagrams for empirical research

Biometrika ◽  
1995 ◽  
Vol 82 (4) ◽  
pp. 692-693
Author(s):  
DAVID FREEDMAN
Keyword(s):  
Empirical Research ◽  
Causal Diagrams

Testing Causal Theories with Learned Proxies

2022 ◽  
Vol 25 (1) ◽  
Author(s):  
Dean Knox ◽  
Christopher Lucas ◽  
Wendy K. Tam Cho
Keyword(s):  
Political Science ◽  
Best Practice ◽  
Computational Models ◽  
Annual Review ◽  
Publication Date ◽  
Social Scientists ◽  
Theoretical Concepts ◽  
Point Estimates ◽  
Causal Diagrams ◽  
Causal Theories

Social scientists commonly use computational models to estimate proxies of unobserved concepts, then incorporate these proxies into subsequent tests of their theories. The consequences of this practice, which occurs in over two-thirds of recent computational work in political science, are underappreciated. Imperfect proxies can reflect noise and contamination from other concepts, producing biased point estimates and standard errors. We demonstrate how analysts can use causal diagrams to articulate theoretical concepts and their relationships to estimated proxies, then apply straightforward rules to assess which conclusions are rigorously supportable. We formalize and extend common heuristics for “signing the bias”—a technique for reasoning about unobserved confounding—to scenarios with imperfect proxies. Using these tools, we demonstrate how, in often-encountered research settings, proxy-based analyses allow for valid tests for the existence and direction of theorized effects. We conclude with best-practice recommendations for the rapidly growing literature using learned proxies to test causal theories. Expected final online publication date for the Annual Review of Political Science, Volume 25 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Behaviour study of an eccentric pulley transmission system using systemic methods

DYNA ◽  
2019 ◽  
Vol 86 (210) ◽  
pp. 204-210
Author(s):  
Iñaki Esnal Angulo ◽  
Bernabé Hernandis Ortuño
Keyword(s):  
Transmission System ◽  
Curvature Function ◽  
Angle Variation ◽  
Transmission Process ◽  
Causal Diagrams ◽  
Simulation Scenario ◽  
Human Use ◽  
Behaviour Study ◽  
Validation Tool

This paper describes a case study carried out on the behaviour of an eccentric pulley transmission system housed in a manually operated abdominal-intestinal assistant device for human use. The aim is to establish a systemic framework that serves as a validation tool for mechanical systems in the initial stages of the product design processes. The proposed study system describes the device transmission process as a function of the input angle variation (dα), the height of the user’s feet (HF) and the pulley curvature function (C), resulting from the variation of its radius (dR) over time (dt). In order to explore and compare the different behaviours and identify possible solutions four different configurations of curvatures were proposed. Causal diagrams and differential equations describe the simulation scenario. The resulting application model supports the use of a systemic frame and methods as a pre-response to the validation of design proposals.

scholarly journals Causal Diagrams: Pitfalls and Tips

2020 ◽  
Vol 30 (4) ◽  
pp. 153-162 ◽  
Author(s):  
Etsuji Suzuki ◽  
Tomohiro Shinozaki ◽  
Eiji Yamamoto
Keyword(s):  
Causal Diagrams

scholarly journals Trends in the investigation of social determinants of health: selected themes and methods

2011 ◽  
Vol 27 (1) ◽  
pp. 183-189 ◽  
Author(s):  
Roger Keller Celeste ◽  
João Luiz Bastos ◽  
Eduardo Faerstein
Keyword(s):  
Public Health ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Structural Equation ◽  
Determinants Of Health ◽  
Equation Modeling ◽  
New Methods ◽  
Causal Diagrams ◽  
Pubmed Database ◽  
Race Ethnicity

We analyze bibliometric trends of topics relevant to the epidemiologic research of social determinants of health. A search of the PubMed database, covering the period 1985-2007, was performed for the topics: socioeconomic factors, sex, race/ethnicity, discrimination/prejudice, social capital/support, lifecourse, income inequality, stress, behavioral research, contextual effects, residential segregation, multilevel modeling, regression based indices to measure inequalities, and structural equation modeling/causal diagrams/path analysis. The absolute, but not the relative, frequency of publications increased for all themes. Total publications in PubMed increased 2.3 times, while the subsets of epidemiology/public health and social epidemiologic themes/methods increased by factors of 5.3 and 5.2, respectively. Only multilevel and contextual analyses had a growth over and above that observed for epidemiology/public health. We conclude that there is clearly room for wider use of established techniques, and for new methods to emerge when they satisfy theoretical needs.

scholarly journals A Graphical Approximation to Generalization: Definitions and Diagrams

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Fernando Martel García ◽  
Leonard Wantchekon
Keyword(s):  
External Validity ◽  
Fundamental Problem ◽  
Systematic Variation ◽  
Causal Effects ◽  
Graphical Language ◽  
Causal Diagrams ◽  
Causal Approach

AbstractThe fundamental problem of external validity is not to generalize from one experiment, so much as to experimentally test generalizable theories. That is, theories that explain the systematic variation of causal effects across contexts. Here we show how the graphical language of causal diagrams can be used in this endeavour. Specifically we show how generalization is a causal problem, how a causal approach is more robust than a purely predictive one, and how causal diagrams can be adapted to convey partial parametric information about interactions.

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