alexa Hypothesizing And Testing Causal Relationships In Correlated Time-series Observations | 12080
ISSN: 2157-7617

Journal of Earth Science & Climatic Change
Open Access

Like us on:

Our Group organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
Recommended Conferences

9th World Conference on Earth Science

Beijing, China

2nd Global Summit on Earth Science and Climate Change

Prague, Czech Republic

9th World Climate Congress & Expo

Valencia, Spain
Share This Page

Hypothesizing and testing causal relationships in correlated time-series observations

2nd International Conference on Earth Science & Climate Change

Gary B. Hughes

ScientificTracks Abstracts: J Earth Sci Climate Change

DOI: 10.4172/2157-7617.S1.009

Abstract
Traces of past earth system states are preserved in an array of biogeochemical archives. Extracting information from these archives produces valuable data that reveal time progression of environmental conditions. Covarying measurements entice cause-effect explanations, but establishing causal relationships from observational studies requires a rigorous epistemology. Correlation is necessary for hypothesizing a causal relationship, but insufficient for forming conclusions. Common-cause explanations, such as independent orbital forcing of correlated measurements, must be rejected based on characteristics of the data. Determining amplitude and phase response over a range of frequencies provides a test of cause-effect scenarios: measured cause must precede proportionate effect in a manner consistent with direct forcing theory. Amplitude and phase persistence (coherence) through time provides a method for quantifying probabilistic confidence in a cause-effect conclusion. In this presentation, methods are described, and then applied to ice core proxies for air temperature and atmospheric carbon dioxide concentration.
Biography
Gary B. Hughes is currently Assistant Professor in the Statistics Department at California Polytechnic State University in San Luis Obispo, CA. He has worked in the infrared imaging industry since 1990, performing various functions in mechanical, software, manufacturing, reliability and systems engineering. Since 1993, he has also taught and performed research at several academic institutions, including Cal Poly San Luis Obispo, UC Santa Barbara and Cal State Channel Islands. He completed a Ph.D. in Earth & Environmental Science at the University of Pennsylvania; a M.A. in Applied Mathematics at UC Santa Barbara; and a B.A. in Mathematics at Northwestern University.
Top