skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Causal inference in cumulative risk assessment: The roles of directed acyclic graphs

Authors:
; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1418733
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Environment International
Additional Journal Information:
Journal Volume: 102; Journal Issue: C; Related Information: CHORUS Timestamp: 2018-01-30 19:22:34; Journal ID: ISSN 0160-4120
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Brewer, L. Elizabeth, Wright, J. Michael, Rice, Glenn, Neas, Lucas, and Teuschler, Linda. Causal inference in cumulative risk assessment: The roles of directed acyclic graphs. United States: N. p., 2017. Web. doi:10.1016/j.envint.2016.12.005.
Brewer, L. Elizabeth, Wright, J. Michael, Rice, Glenn, Neas, Lucas, & Teuschler, Linda. Causal inference in cumulative risk assessment: The roles of directed acyclic graphs. United States. doi:10.1016/j.envint.2016.12.005.
Brewer, L. Elizabeth, Wright, J. Michael, Rice, Glenn, Neas, Lucas, and Teuschler, Linda. Mon . "Causal inference in cumulative risk assessment: The roles of directed acyclic graphs". United States. doi:10.1016/j.envint.2016.12.005.
@article{osti_1418733,
title = {Causal inference in cumulative risk assessment: The roles of directed acyclic graphs},
author = {Brewer, L. Elizabeth and Wright, J. Michael and Rice, Glenn and Neas, Lucas and Teuschler, Linda},
abstractNote = {},
doi = {10.1016/j.envint.2016.12.005},
journal = {Environment International},
number = C,
volume = 102,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2017},
month = {Mon May 01 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.envint.2016.12.005

Citation Metrics:
Cited by: 1work
Citation information provided by
Web of Science

Save / Share:
  • Markov chain Monte Carlo approaches to sampling directly from the joint posterior distribution of aleatory model parameters have led to tremendous advances in Bayesian inference capability in a wide variety of fields, including probabilistic risk analysis. The advent of freely available software coupled with inexpensive computing power has catalyzed this advance. This paper examines where the risk assessment community is with respect to implementing modern computational-based Bayesian approaches to inference. Through a series of examples in different topical areas, it introduces salient concepts and illustrates the practical application of Bayesian inference via Markov chain Monte Carlo sampling to a varietymore » of important problems.« less
  • In designing VLSI architectures for a complex computational task, the functional decomposition of the task into a set of computational modules can be represented as a directed task graph, and the inclusion onf input dta modifies the task graph to an acylic data flow graph (ADFG). Due to different paths of traveling and computation time of each computational module, operands may arrive at multiinput modules at different arrival times, causing a longer pipelined time. Delay buffers may be inserted along various paths to balance the ADFG to achieve maximum pipelining. This paper presents an efficient decomposition technique which provides amore » more systematic approach in solving the optimal buffer assignment problem of an ADFG with a large number of computational nodes. The buffer assignment problem is formulated as an integer linear optimization problem which can be solved in pseudopolynomial time. Examples are given to illustrate the proposed decomposition technique.« less
  • Sponsored by five major scientific organization, the attracted Forum on Global Change and Our Common Future held in Washington, DC, attracted more than 1200 participants-a large number for any meeting on global change-and included a sizeable representation from outside the usual scientific and affiliated circles. The meeting was intended to engrave global change on the public-policy agenda and provide a short course on its science and consequences: the accumulation of greenhouse gases in the atmosphere, the depletion of stratospheric ozone over Antarctica, and deforestation and the accompanying loss of species. Projecting scenarios of global change, and possible responses, is anmore » exercise in risk assessment, replete with statements of probabilities, uncertainties, and even scientific intuition. Still, the message to emerge from the forum and other recent meetings is that there is agreement in the scientific community that the Earth is slated for a 2-6{degree}C increase in global average temperature from a doubling of carbon dioxide during the next 50 to 100 years and that this unprecedented predicted rate of change warrants immediate action.« less
  • Environmental scientists and managers must determine whether a relationship between an environmental factor and an observed effect is causal and respond accordingly. Epidemiologists have, over the past 150 yr, developed a systematic approach to evaluating these relationships. Their criteria for objectively evaluating the relationship between a suspect cause and a chronic disease are (1) probability, (2) time order, (3) strength of association, (4) specificity, (5) consistency on replication, (6) predictive performance, and (7) coherence. These criteria can be used, with little modification, to evaluate associations in relation to diseases in fish and wildlife suspected to be caused by exposure tomore » chemical pollutants. Some populations of fish and wildlife are members of the same guilds as subpopulations of humans. Investigations of chemically induced disease in these sentinel populations of fish and wildlife may identify the potential risks posed to these human subpopulations. Evidence evaluated using the epidemiologic criteria may assist environmental managers to determine whether a substantive case can be made to initiate preventative or remedial action. By applying the null hypothesis, scientists are forced to consider how much information must be ignored to conclude that a causal relationship does not exist.« less