(1) define causal interventions and actions, draw and interpret causal diagrams, and apply the rules of causal diagrams to distinguish causal from non-causal statistical associations.

(2) decide which biostatistical/epidemiological methods must be used in different situations to derive causal effect parameters.

(3) decide which structural and statistical approach should be applied to estimate the effects of dynamic treatments using observational data.

(4) understand the conceptual approaches of causal modeling

(5) know how these methods are applied in big real world data and pragmatic trials

(6) know the principles of causal discovery and their underlying assumptions

Published cardiovascular, oncology, HIV, nutrition and obstetrics examples will be used to demonstrate how to:

• Adjust for time-independent confounders (i.e., confounder affects both risk factor and disease), where standard stratification, regression analysis, propensity score methods and matching/balancing approaches yield valid causal effects if all confounders are measured, and
• Adjust for time-dependent confounding (i.e., the confounder simultaneously acts as an intermediate step in the causal chain between risk factor and disease), where standard regression analysis fails and causal g-methods such as g-formula, inverse probability weighting of marginal structural models or g-estimation of structural nested models must be used.
• Adjust for non-adherence in randomized clinical trials, where both the intention-to-treat and the naïve per protocol analyses can fail to yield the true causal intervention effect;
• Assess the “fallibility of estimating direct effects” when adjusting for intermediate steps;
• Derive a causal graph from the observed data using systematic search algorithms.
• Discuss different biases such as time-independent confounding, time dependent confounding, selection bias, and immortal time bias etc.