Executive Development Programme in Identifying Causal Links in Data

This program is designed for executives and data professionals seeking to enhance their ability to identify and understand causal relationships within complex data sets. Participants will learn advanced statistical and causal inference techniques, enabling them to make more informed decisions based on robust data analysis.

Upon completion, attendees will gain skills in applying causal models, interpreting causal effects, and integrating causal reasoning into strategic planning and business operations.

1. 1. Introduction to Causal Inference: Learners will explore the fundamentals of causal inference, including the difference between correlation and causation, and understand the importance of identifying causal links in data. Practical skills include recognizing bias and confounding variables.
2. 2. Causal Graphs and Directed Acyclic Graphs (DAGs): This module focuses on using graphical models to represent causal relationships and how to interpret and manipulate these graphs. Learners will gain the ability to construct and analyze DAGs to identify direct and indirect causal effects.
3. 3. Potential Outcomes Framework: Learners will study the potential outcomes framework and its application in causal inference. This includes understanding the concept of counterfactuals and how to design experiments to estimate causal effects.
4. 4. Propensity Score Analysis: This module covers the use of propensity scores in estimating causal effects, particularly in observational studies. Practical skills include calculating propensity scores and using them to adjust for selection bias.
5. 5. Instrumental Variables: Learners will delve into the instrumental variables method for addressing endogeneity in causal relationships. They will learn how to identify and use instrumental variables to obtain consistent causal estimates.
6. 6. Regression Discontinuity Design (RDD): This module introduces regression discontinuity design as a quasi-experimental method to identify causal effects. Learners will learn how to design and analyze RDDs to estimate causal impacts.
7. 7. Difference-in-Differences (DiD) Analysis: This module focuses on the difference-in-differences approach for comparing outcomes over time in a pre-post analysis. Learners will gain the skills to set up and interpret DiD models to assess causal impact.
8. 8. Advanced Causal Inference Techniques: In this module, learners will explore advanced topics such as mediation analysis and propensity score matching. They will learn how to apply these techniques to more complex causal questions.
9. 9. Causal Inference in Big Data: This module covers the challenges and methods for causal inference in big data settings. Learners will learn how to handle large datasets, deal with data sparsity, and apply causal inference techniques effectively in big data contexts.
10. 10. Case Studies in Causal Inference: The final module involves real-world case studies where learners apply the concepts and skills learned throughout the programme. They will analyze data, identify causal links, and present findings in a professional setting.