about this book
This book is designed for beginner or experienced data scientists, machine learning practitioners and researchers, data analysts, economists, and statisticians who want to improve their decision-making using observational data. It aims to give you a strong foundation in applying causal inference in your everyday tasks. It offers an intuitive guide to understanding which tools to use and, coupled with a more formal approach, ensures that you’re confident in your actions.
Prerequisites
To follow along with the book, you will need a basic background in the following:
- Probability
- Basic probability formulas such as the law of total probability and conditional probabilities
- Basic probability distributions such as gaussians and binomials
- How to generate random numbers with a computer
- Statistics
- Linear and logistic regression
- Confidence intervals
- Recommended: basic knowledge of A/B testing and randomized controlled trials (how group assignment is done and hypothesis testing)
- Programming
- Basic coding skills (reading/writing basic programs) with at least one programming language such as Python, R, or Julia
- Machine learning
- Cross-validation and hyperparameter tuning
- Recommended: Experience with machine learning models such as kNN, random forests, boosting, and deep learning