Executive Development Programme in Causal Inference in Data Science

This course is designed for data scientists, business analysts, and executives seeking to enhance their ability to make data-driven decisions through causal inference. Participants will gain proficiency in identifying cause-and-effect relationships, using advanced techniques like propensity score matching, instrumental variables, and difference-in-differences, to inform strategic business decisions.

By the end of the program, attendees will be equipped to critically evaluate causal claims in research, design robust experiments, and apply causal inference methods to real-world data, thereby improving the effectiveness of their analytics and driving more impactful business outcomes.

1. 1. Introduction to Causal Inference: Learners will understand the basics of causal inference, including its importance in data science, and learn how to formulate causal questions. They will gain foundational knowledge of potential outcomes, causal diagrams, and the distinction between correlation and causation.
2. 2. Fundamentals of Probability and Statistics: This module covers essential probability and statistical concepts necessary for understanding causal inference. Learners will study probability distributions, statistical inference, and hypothesis testing, equipping them with the tools to analyze data robustly.
3. 3. Potential Outcomes Framework: Learners will delve into the potential outcomes framework, a cornerstone of causal inference, and learn how to define and estimate causal effects. They will gain practical skills in identifying and addressing selection bias.
4. 4. Causal Graphical Models: This module introduces learners to causal graphical models, including directed acyclic graphs (DAGs), and how they can be used to represent causal relationships and perform causal inference. Learners will learn to identify confounders and learn the principles of causal mediation and interaction analysis.
5. 5. Estimation Methods in Causal Inference: This module covers various methods for estimating causal effects, including propensity score matching, inverse probability weighting, and instrumental variables. Learners will gain hands-on experience with these techniques and understand their assumptions and limitations.
6. 6. Advanced Topics in Causal Inference: Learners will explore advanced topics such as causal forests, Bayesian causal inference, and the integration of causal inference with machine learning. They will learn to apply these methods to complex data sets and interpret the results.
7. 7. Mediation and Moderation Analysis: This module focuses on mediation and moderation analysis, essential for understanding the mechanisms behind causal effects. Learners will learn how to decompose total effects into direct and indirect effects and how to identify moderation effects.
8. 8. Interrupted Time Series Analysis: Learners will study interrupted time series analysis, a method for evaluating the impact of interventions over time. They will learn to design and analyze time series data to assess causal effects accurately.
9. 9. Causal Inference with Machine Learning: This module explores the intersection of causal inference and machine learning, including methods like doubly robust estimation and causal forests. Learners will learn how to leverage machine learning techniques to improve causal estimates.
10. 10. Practical Applications and Case Studies: The final module provides learners with real-world case studies and practical applications of causal inference in various industries. They will work on projects to apply their knowledge to complex data sets, preparing them for professional challenges in the field.