Executive Development Programme in Causal Linkage Analysis Methods

This course is designed for senior business leaders and data analysts seeking to enhance their decision-making capabilities through advanced causal linkage analysis methods. Participants will gain skills in identifying and quantifying cause-and-effect relationships, enabling them to develop more effective strategies and policies.

By the end of the program, attendees will be proficient in applying causal inference techniques to real-world business scenarios, thereby improving predictive accuracy and strategic foresight.

1. 1. Introduction to Causal Inference: Learners will explore the basics of causal inference, understanding potential outcomes and the counterfactual framework. They will gain foundational skills in identifying and specifying causal questions.
2. 2. Causal Diagrams and Directed Acyclic Graphs (DAGs): This module covers the use of causal diagrams and DAGs for visualizing and understanding causal relationships. Learners will learn to construct and interpret these graphs to identify confounding variables and effect modifiers.
3. 3. Propensity Score Analysis: Learners will study propensity score methods for estimating causal effects in observational studies. They will gain practical skills in using propensity scores to adjust for confounding and estimate treatment effects.
4. 4. Instrumental Variables: This module focuses on instrumental variables as a method to address endogeneity. Learners will understand the role of instruments and how to use them to identify causal effects in the presence of unobserved confounding.
5. 5. Regression Discontinuity Design (RDD): Learners will explore the regression discontinuity design as a quasi-experimental method for estimating causal effects. They will learn to apply RDD to real-world data and interpret the results.
6. 6. Difference-in-Differences (DiD): This module covers the difference-in-differences approach to evaluate the impact of interventions. Learners will learn to design and implement DiD analyses to estimate causal effects of policy changes.
7. 7. Synthetic Control Method: Learners will study the synthetic control method for causal inference in settings with multiple treated units. They will gain skills in constructing synthetic controls and interpreting results in policy evaluation.
8. 8. Machine Learning for Causal Inference: This module introduces machine learning techniques for causal inference, including propensity score matching, regression trees, and ensemble methods. Learners will learn to apply these methods to enhance causal effect estimation.
9. 9. Advanced Topics in Causal Machine Learning: Learners will delve into advanced topics such as doubly robust methods, targeted maximum likelihood estimation, and causal forests. They will gain expertise in using advanced causal inference methods to address complex research questions.
10. 10. Practical Application and Reporting of Causal Findings: This final module focuses on applying the learned methods to real-world datasets and effectively communicating causal findings. Learners will work on case studies and learn best practices for reporting and presenting causal analyses.