Advances in Causal Understanding for Human Health Risk-Based Decision Making

Posted on

March 6-7, 2017

The National Academies of Sciences, Engineering, and Medicine
2101 Constitution Avenue Northwest
Washington, DC

New molecular and bioinformatic approaches have advanced understanding of how molecular pathways are affected by exposure and the molecular networks involved in disease. However, these advances are often not yet deemed sufficient to establish causality for public health risk assessments; regulators still rely primarily on traditional apical endpoints, such as those endpoints observed in animal studies.


This workshop discussed the current thinking surrounding causal models, how novel approaches and tools are relevant for environmental health, and how they can be incorporated into the decision making process. Held in Washington, DC and webcast, this workshop brought together leading environmental health experts, toxicologists, statisticians, sociologists, epidemiologists, regulators and experts from other fields that utilize different data streams for establishing causality in complex systems.


This National Academy of Sciences activity was sponsored by the National Institutes of Environmental Health Sciences (NIEHS).

Final agenda book



Video Playlist (YouTube)

  • Welcome—Kimberly Thigpen Tart, National Institute of Environmental Health Sciences // download PDF
  • Opening Remarks—Kim Boekelheide, Brown University, Standing Committee Co-Chair // download PDF
  • Causal Inference: New Data, An Old Problem— Jonathan Samet, University of Southern California Institute for Global Health // download PDF
  • Causal Inference from Data—Philip Stark, University of California, Berkeley // PDF not available
  • Causal Models in Epidemiology—Paolo Vineis, Imperial College London  // download PDF
  • The Key Characteristics of Carcinogens—Martyn Smith, University of California, Berkeley, School of Public Health // PDF not available
  • Inferring Causality in Observational Epidemiology: Breast Cancer Risk as an Example—Mary Beth Terry, Columbia University Mailman School of Public Health // download PDF
  • Determining Causality in Obesity—Jessie Buckley, Johns Hopkins Bloomberg School of Public Health //download PDF
  • The Negative Control Approach to Detect and Correct for Unobserved Confounding—Eric Tchetgen Tchetgen, Harvard T.H Chan School of Public Health //  download PDF
  • 20th Century Causality Frameworks Are Evolving To Fit 21st Century Data—Vincent Cogliano, U.S. Environmental Protection Agency // PDF not available
  • The AOP Framework and Causality: Meeting Chemical Risk Assessment Challenges in the 21st Century—Gerald Ankley, U.S. Environmental Protection Agency // PDF not available
  • Computational Causal Discovery—Richard Scheines, Carnegie Mellon University // PDF not available
  • A Big Tech Approach to a “Small” Problem: Microbiome Characterization of Raw Food Ingredients to Improve Food Safety—Kristen Beck, IBM Watson // PDF not available
  • Day 2, Welcome—Gary Miller, Emory University //download PDF
  • Debate Scenario 1: Mercury and Cardiovascular Health
    – Gary Ginsberg, Connecticut Department of Public Health //  download PDF
    – Melissa Perry, George Washington University //  download PDF
  • Debate Scenario 2: Application of Read-Across Using in vitro Data in Dodecylphenol Risk Assessment // download PDF
    − Lesa Aylward, Summit Toxicology, LLP  // watch video
    − Patrick McMullen, ScitoVation // watch video
    Debate 2 Q&A // watch video
  • Debate Scenario 3: Application of Human CellBased Assays in Toxicity Testing
    − Reza Rasoulpour, Dow AgroSciences // download PDF
    − Norbert Kaminski, Michigan State University // download PDF
    Debate 3 Q&A // watch video
  • Panel Discussion to Identify Unifying Themes − Gary Ginsberg, Connecticut Department of Public Health; Melissa Perry, George Washington University; Lesa Aylward, Summit Toxicology, LLP; Patrick McMullen, ScitoVation; Reza Rasoulpour, Dow AgroSciences; Norbert Kaminski, Michigan State University // watch video