Quantifying and Reducing Uncertainty About Causality in Improving Public Health and Safety

  • Louis Anthony CoxJr.Email author
Reference work entry


Effectively managing uncertain health, safety, and environmental risks requires quantitative methods for quantifying uncertain risks, answering the following questions about them, and characterizing uncertainties about the answers:
  • Event detection: What has changed recently in disease patterns or other adverse outcomes, by how much, when?

  • Consequence prediction: What are the implications for what will probably happen next if different actions (or no new actions) are taken?

  • Risk attribution: What is causing current undesirable outcomes? Does a specific exposure harm human health, and, if so, who is at greatest risk and under what conditions?

  • Response modeling: What combinations of factors affect health outcomes, and how strongly? How would risks change if one or more of these factors were changed?

  • Decision making: What actions or interventions will most effectively reduce uncertain health risks?

  • Retrospective evaluation and accountability: How much difference have exposure reductions actually made in reducing adverse health outcomes?

These are all causal questions. They are about the uncertain causal relations between causes, such as exposures, and consequences, such as adverse health outcomes. This chapter reviews advances in quantitative methods for answering them. It recommends integrated application of these advances, which might collectively be called causal analytics, to better assess and manage uncertain risks. It discusses uncertainty quantification and reduction techniques for causal modeling that can help to predict the probable consequences of different policy choices and how to optimize decisions. Methods of causal analytics, including change-point analysis, quasi-experimental studies, causal graph modeling, Bayesian Networks and influence diagrams, Granger causality and transfer entropy methods for time series, and adaptive learning algorithms provide a rich toolkit for using data to assess and improve the performance of risk management efforts by actively discovering what works well and what does not.


Adaptive learning Change-point analysis (CPA) Bayesian networks (BN) Causal analytics Causal graph Causal laws Counterfactual Directed acyclic graph (DAG) DAG model Dynamic Bayesian networks (DBN) Ensemble learning algorithms Evaluation analytics Granger causality Influence diagram (ID) Intervention analysis Interrupted time series analysis Learning analytics Marginal structural model (MSM) Model ensembles Multi-agent influence diagram (MAID) Path analysis Predictive analytics Prescriptive analytics Propensity score Quasi-experiments (QEs) Simulation Structural equations model (SEM) Structure discovery Transfer Entropy Uncertainty analytics 


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Cox Associates and University of ColoradoDenverUSA

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