Causal and mathematical models are widely used for decision making and policy evaluation at both the micro and macro levels. For example, causal models using large datasets are used to evaluate treatment efficacy in HIV; mathematical models are used to simulate the effects of prevention or policy measures to improve health outcomes or reduce the spread of infectious diseases. Entities such as the World Health Organization and UNAIDS rely on these models to set wide-ranging and high-impact policy related to treatment and prevention of infectious disease.

Causal models tend to rely on large-scale cohort data, while mathematical models in many ways represent evidence synthesis. Important methodologic issues in the development, application, and interpretation of these models include the role of untestable assumptions, transportability of findings to specific populations of interest, model calibration and validation, and uncertainty quantification. The datasets used to develop these models are complex in nature.

This workshop will bring together leading researchers in the areas of modeling, machine learning and causal inference to delve more deeply into foundational and methodologic issues and their implications, illustrate the use of these models in real-world settings, and draw connections between the two approaches.

Key questions to be addressed and discussed include:

• What is the role of an underlying causal model in decision making?


• How do we quantify uncertainty from multiple sources, such as model selection, untestable assumptions, prediction uncertainty?


• What is the role of predictive models in causal inference?


• What are the connections between statistical models and mathematical agent-based models for drawing causal inferences?


• What is the role of machine learning in inferring causal relationships and decision making?


• How should methods be adapted to specialized settings (e.g., social networks)?