Causal Inference for Data Science, Ruiz A., Robert V., 2025

Causal Inference for Data Science, Ruiz A., Robert V., 2025.
    
   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.




Create a model.
Once you have all the important factors, you create a causal model that attempts to isolate the true causal effect by accounting for potential confounding variables that might influence both the “cause” and “effect” variables. Causal models are conceptual frameworks often represented using Directed Acyclic Graphs (DAGs) which visually depict the causal relationships between variables, including potential mediating variables and confounders. In a causal DAG, variables are represented as nodes and causal relationships between them as arrows. You’ll learn more about causal modeling with DAGs in chapter 3, and we will use them throughout most of this book. Another approach to causal modeling is to use equations, which we’ll talk about in chapter 10.

You can go for simpler models with fewer factors and fewer assumptions. But if the model is too simple, it may not work well in the real world and won’t help you make accurate predictions. On the other hand, more complex models can be hard to handle in terms of mathematical formulas, computational methods, or checking whether complex assumptions hold in reality.

Contents.
Preface.
Acknowledgments.
About this book.
About the author.
About the cover illustration.
Part l Inference and the role of Confounders.
1 Introducing causality.
2 First steps: Working with confounders.
3 Applying causal inference.
4 Ноw machine learning and causal inference can help each other.
Part 2 The adjustment formula in practice.
5 Finding comparable cases with propensity scores.
6 Direct and indirect effects with linear models.
7 Dealing with complex graphs.
8 Advanced tools with the DoubleML library.
9 Instrumental variables.
10 Potential outcomes framework.
11 The effect of a time-related event.
Appendix A The math behind the adjustment formula Appendix В Solutions to exercises in chapter 2.
B.1 Solution to Simpson’s paradox for treatment В.
B.2 Observe and do are different things.
B.2.1 Solution.
B.3 What do we need to adjust?.
B.3.1 RCT.
B.3.2 Confounder.
B.3.3 Unobserved Confounder.
B.3.4 Mediators.
B.3.5 Outcome predictive variables.
Appendix C Technical lemma for the propensity scores.
Appendix D Prooffor doubly robust.
D.2 Doubly robust property with respect to inverse probability weighting.
Appendix E Technical lemma for the alternative instrumental variable estimator.
Appendix F Proof of the instrumental variable formula for imperfect compliance.
Index.



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