Module 3: Causal Inference and Programme Evaluation with A/B Testing & Multi-Armed Bandits
About This Course
In any organisation, strategic decision makers commonly face questions about the impact of a certain strategy on their target groups of people. This requires expertise in causal inference. It is notable, however, that causal inference is often missed in a Python course on computational business analytics.
Causal inference can be used to make information that can help improve user experience and generate business decisions by knowing its impact on the business.
In this module, participants will learn when business analytics require casual inference, how to execute in Python, and how to interpret the results. Additionally, they will also learn the logic of causation, fundamentals of A/B testing and analysis, and econometric methods for analysing causality in naturally arising data.
What You'll Learn
• Explain the meaning of causation and directed acyclic graphs (DAGs)
• Identify major threats to causal inference and program evaluation
• Run statistical models for A/B tests data
• Execute econometric analyses (Difference-in-Differences, Regression Discontinuity, Instrumental Variable, and Matching)
Entry Requirements
Participants should have some knowledge or experience in Python programming (equivalent to that attained in SMU Academy's Professional Certificate in Python Programming programme).