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

(3) to use causal diagrams to estimate the direction of bias in "non-causal" models.

(4) to understand the conceptual approaches of causal modeling

(5) to know how these methods are applied in big real world data

Published cardiovascular, HIV, cancer, nutrition and obstetrics examples will be used to:

• Adjust for compliance in randomized clinical trials, where both "intention to treat" and "per protocol" analyses can fail to yield the true causal intervention effect;
• Assess the “fallibility of estimating direct effects” (i.e., adjusting for intermediate steps);
• Adjust for time-independent confounders in observational studies (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 in observational studies (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 methods" such as marginal structural models or g-estimation must be used.
• Assess unmeasured confounding