Multiple Indicators and Complex Causal Models

2017 ◽  
pp. 395-402
Author(s):  
John L. Sullivan
Keyword(s):  
Causal Models ◽  
Multiple Indicators

Review of Multiple indicators: An introduction.

1980 ◽  
Vol 25 (6) ◽  
pp. 505-505
Author(s):  
GEOFFREY KEPPEL
Keyword(s):  
Multiple Indicators

A Program to Help Find Causal Models of Nonexperimental Data

1989 ◽  
Vol 34 (12) ◽  
pp. 1086-1087
Author(s):  
Charles F. Chubb
Keyword(s):  
Causal Models

Maltreatment Variation and Trends in Six Developed Countries Using Multiple Indicators: Methods and Policy Implications

2012 ◽  
Author(s):  
Ruth Gilbert ◽  
John D. Fluke ◽  
Melissa O'Donnell ◽  
Arturo Gonzalez-Izquierdo ◽  
Marni Brownell ◽  
...  
Keyword(s):  
Developed Countries ◽  
Policy Implications ◽  
Multiple Indicators

The Role of Causal Models in Analogical Inference

2010 ◽  
Vol 42 (8) ◽  
pp. 834-844
Author(s):  
Ting-Ting WANG ◽  
Lei MO
Keyword(s):  
Causal Models ◽  
Analogical Inference

scholarly journals Segment-Based Approach for Assessing Hazard Risk of Coastal Highways in Hawai‘i

2019 ◽  
Vol 2673 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Harrison Togia ◽  
Oceana P. Francis ◽  
Karl Kim ◽  
Guohui Zhang
Keyword(s):  
Qualitative Method ◽  
Data Sets ◽  
Regional Environment ◽  
The Road ◽  
Hazard Exposure ◽  
Multiple Indicators ◽  
System Data ◽  
Rural Highway ◽  
Geographic Information System Data

Hazards to roadways and travelers can be drastically different because hazards are largely dependent on the regional environment and climate. This paper describes the development of a qualitative method for assessing infrastructure importance and hazard exposure for rural highway segments in Hawai‘i under different conditions. Multiple indicators of roadway importance are considered, including traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations. The method of evaluating roadway hazards and importance can be tailored to fit different regional hazard scenarios. It assimilates data from diverse sources to estimate risks of disruption. A case study for Highway HI83 in Hawai‘i, which is exposed to multiple hazards, is conducted. Weakening of the road by coastal erosion, inundation from sea level rise, and rockfall hazards require adaptation solutions. By analyzing the risk of disruption to highway segments, adaptation approaches can be prioritized. Using readily available geographic information system data sets for the exposure and impacts of potential hazards, this method could be adapted not only for emergency management but also for planning, design, and engineering of resilient highways.

Independence Relations Produced by Parameter Values in Causal Models

1990 ◽  
Vol 18 (2) ◽  
pp. 55-70 ◽  
Author(s):  
Clark Glymour ◽  
Peter Spirtes ◽  
Richard Scheines ◽  
Keyword(s):  
Causal Models ◽  
Parameter Values ◽  
Independence Relations

scholarly journals Testing Graphical Causal Models Using the R Package “dagitty”

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Ankur Ankan ◽  
Inge M. N. Wortel ◽  
Johannes Textor
Keyword(s):  
R Package ◽  
Causal Models
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