This repository serves as a resource for a course focused on employing causal data analysis to make informed business decisions. It covers causal methods, their appropriate contexts, implementation in Python, and application to various business domains including pricing, reputation system design, social networks, advertising, and algorithm optimization.
- Understand the principles of causal data analysis.
- Learn Python implementations of causal methods.
- Apply causal analysis to real-world business scenarios such as pricing adjustments, algorithm changes, and more.
- Python Notebooks demonstrating causal analysis techniques.
- Case studies on pricing, advertising effectiveness, and other business-related topics.
- Additional resources and readings on causal methods in data science.
To interact with the materials, you will need:
- Python 3.x
- Libraries: numpy, pandas, matplotlib, seaborn, statsmodels, scipy