Tony Cox
President, Cox Associates
Clinical Professor, University of Colorado

Title: Causal Analytics for Risk Management: Making Advanced Analytics More Useful

Operations research provides powerful methods for choosing feasible values of decision variables to optimize an objective function. In practice, however, how decisions affect the objective function, and even what decisions are feasible, are often initially unknown. Managing risks effectively usually requires using available data, however limited, to answer the following questions, and then improve the answers in light of experience:

1. DESCRIPTIVE ANALYTICS: What's happening? What has changed recently? What should we be worrying about?
2. PREDICTIVE ANALYTICS: What will (probably) happen if we do nothing new?
3. CAUSAL ANALYTICS: What will (probably) happen if we take different actions or implement different policies? How soon are the consequences likely to occur, and how sure can we be?
4. PRESCRIPTIVE ANALYTICS: What should we do next? How should we allocate available resources to explore, evaluate, and implement different actions or policies in different locations?
5. EVALUATION ANALYTICS: How well are our risk management policies and decisions working? Are they producing (only) their intended effects? For what conditions or sub-populations do they work or fail?
6. LEARNING ANALYTICS: How might we do better, taking into account value of information and opportunities to learn from small trials before scaling up?
7. COLLABORATIVE ANALYTICS: How can we manage uncertain risks more effectively together?

This talk discusses recent advances in these areas and suggests how to integrate and apply them to important policy questions such as whether, when, and how to revise risk management regulations or policies. Current technical methods of risk analytics, including change point analysis and prescriptive maintenance, quasi-experimental design and analysis, causal graph modeling, Bayesian Networks and influence diagrams, Granger causality and transfer entropy methods for time series, causal analysis and modeling, and deep learning and low-regret learning provide a valuable toolkit for using data to assess and improve the performance of risk management decisions and policies by actively discovering what works best and how to improve over time.

Tony Cox is President of Cox Associates, a Denver-based applied research company specializing in health, safety, and environmental risk analysis; epidemiology; policy analytics; data science; and operations research. Since 1986, Cox Associates' analysts and scientists have applied epidemiological, risk analysis, and operations research models to measurably improve health and environment risk assessment and decision-making for public and private sector clients. In 2006, Cox Associates was inducted into the Edelman Academy of the Institute for Operations Research and Management Science (INFORMS), recognizing outstanding real-world achievements in the practice of operations research and the management sciences. In 2012, Dr. Cox was inducted into the National Academy of Engineering (NAE), "For applications of operations research and risk analysis to significant national problems." He has served as a member of the National Academies' Board on Mathematical Sciences and their Applications (BMSA) (2012-2016). In 2013, he co-founded NextHealth Technologies, a Denver-based company offering advanced data analytics solutions to healthcare plans to reduce health, financial, and member attrition risks.