Calling all data scientists and statisticians. Let's get technical for the next SAS Bowl. Causal analysis is a field of statistics and experimental design that involves identifying and understanding the causes of an event. The goal of a causal analysis is to quantify the causal link between treatment and the outcome of interest. SAS has multiple procedures and tools to assist with causal analysis and we'll explore more about the techniques and benefits in December's SAS Bowl.
Identifying causes
Causal analysis can help identify the causes of an event, both individual and organizational.
Distinguishing causality
Causal analysis can help distinguish between direct and indirect causality.
Assessing research
Causal analysis can help assess the assumptions, merits, limitations, and key issues in research.
Drawing conclusions
Causal analysis can help draw conclusions about whether a treatment caused an effect.
Predicting outcomes
Causal analysis can help businesses predict outcomes like product demand and inventory shrinkage.
Identify the key challenge or setback.
Determine the causes and effects of the key challenge.
Below are portal-related resources we'll use to create questions for the game.
For those who may be new to the SAS Bowl, you can find game history and specifics in this Community memo. There you'll also find links to previous events, which include recordings.
Register for the event and receive an invite with game details and a Teams meeting link. On game day, Join the TEAMS meeting to play, and show off your SAS and worldly knowledge while competing for bragging rights and SAS Community game gear.