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Causal Inference and Discovery in Python


CAUSAL INFERENCE AND DISCOVERY IN PYTHON


By Aleksander Molak


May 2023 | 387 pages

Read now
Examine Pearlian causal concepts such as structural causal models,
interventions, counterfactuals, and more
Discover modern causal inference techniques for average and heterogenous
treatment effect estimation
Explore and leverage traditional and modern causal discovery methods
Read now
1 Part 1: Causality – an Introduction


Part 1: Causality – an Introduction

2 Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother?


Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother?

A brief history of causality

Why causality? Ask babies!

How not to lose money… and human lives

Wrapping it up

References

3 Chapter 2: Judea Pearl and the Ladder of Causation


Chapter 2: Judea Pearl and the Ladder of Causation

From associations to logic and imagination – the Ladder of Causation

Associations

What are interventions?

What are counterfactuals?

Extra – is all machine learning causally the same?

Wrapping it up

References

4 Chapter 3: Regression, Observations, and Interventions


Chapter 3: Regression, Observations, and Interventions

Starting simple – observational data and linear regression

Should we always control for all available covariates?

Regression and structural models

Wrapping it up

References

5 Chapter 4: Graphical Models


Chapter 4: Graphical Models

Graphs, graphs, graphs

What is a graphical model?

DAG your pardon? Directed acyclic graphs in the causal wonderland

Sources of causal graphs in the real world

Extra – is there causality beyond DAGs?

Wrapping it up

References

6 Chapter 5: Forks, Chains, and Immoralities


Chapter 5: Forks, Chains, and Immoralities

Graphs and distributions and how to map between them

Chains, forks, and colliders or…immoralities

Forks, chains, colliders, and regression

Wrapping it up

References

7 Part 2: Causal Inference


Part 2: Causal Inference

8 Chapter 6: Nodes, Edges, and Statistical (In)dependence


Chapter 6: Nodes, Edges, and Statistical (In)dependence

You’re gonna keep ‘em d-separated

Estimand first!

The back-door criterion

The front-door criterion

Are there other criteria out there? Let’s do-calculus!

Wrapping it up

Answer

References

9 Chapter 7: The Four-Step Process of Causal Inference


Chapter 7: The Four-Step Process of Causal Inference

Introduction to DoWhy and EconML

Step 1 – modeling the problem

Step 2 – identifying the estimand(s)

Step 3 – obtaining estimates

Step 4 – where’s my validation set? Refutation tests

Full example

Wrapping it up

References

10 Chapter 8: Causal Models – Assumptions and Challenges


Chapter 8: Causal Models – Assumptions and Challenges

I am the king of the world! But am I?

Positivity

Exchangeability

…and more

Call me names – spurious relationships in the wild

Wrapping it up

References

11 Chapter 9: Causal Inference and Machine Learning – from Matching to
Meta-Learners


Chapter 9: Causal Inference and Machine Learning – from Matching to
Meta-Learners

The basics I – matching

The basics II – propensity scores

Inverse probability weighting (IPW)

S-Learner – the Lone Ranger

T-Learner – together we can do more

X-Learner – a step further

Wrapping it up

References

12 Chapter 10: Causal Inference and Machine Learning – Advanced Estimators,
Experiments, Evaluations, and More


Chapter 10: Causal Inference and Machine Learning – Advanced Estimators,
Experiments, Evaluations, and More

Doubly robust methods – let’s get more!

If machine learning is cool, how about double machine learning?

Causal Forests and more

Heterogeneous treatment effects with experimental data – the uplift odyssey

Extra – counterfactual explanations

Wrapping it up

References

13 Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and
Beyond


Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and
Beyond

Going deeper – deep learning for heterogeneous treatment effects

Transformers and causal inference

Causality and time series – when an econometrician goes Bayesian

Wrapping it up

References

14 Part 3: Causal Discovery


Part 3: Causal Discovery

15 Chapter 12: Can I Have a Causal Graph, Please?


Chapter 12: Can I Have a Causal Graph, Please?

Sources of causal knowledge

Scientific insights

Personal experience and domain knowledge

Causal structure learning

Wrapping it up

References

16 Chapter 13: Causal Discovery and Machine Learning – from Assumptions to
Applications


Chapter 13: Causal Discovery and Machine Learning – from Assumptions to
Applications

Causal discovery – assumptions refresher

The four (and a half) families

Introduction to gCastle

Constraint-based causal discovery

Score-based causal discovery

Functional causal discovery

Gradient-based causal discovery

Encoding expert knowledge

Wrapping it up

References

17 Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning
and Beyond


Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning and
Beyond

Advanced causal discovery with deep learning

Causal discovery under hidden confounding

Extra – going beyond observations

Causal discovery – real-world applications, challenges, and open problems

Wrapping it up!

References

18 Chapter 15: Epilogue


Chapter 15: Epilogue

What we’ve learned in this book

Five steps to get the best out of your causal project

Causality and business

Toward the future of causal ML

Learning causality

Let’s stay in touch

Wrapping it up

References

19 Index


Index

Why subscribe?

20 Other Books You May Enjoy


Other Books You May Enjoy

Packt is searching for authors like you

Share Your Thoughts

Download a free PDF copy of this book

1 Part 1: Causality – an Introduction


Part 1: Causality – an Introduction

2 Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother?


Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother?

A brief history of causality

Why causality? Ask babies!

How not to lose money… and human lives

Wrapping it up

References

3 Chapter 2: Judea Pearl and the Ladder of Causation


Chapter 2: Judea Pearl and the Ladder of Causation

From associations to logic and imagination – the Ladder of Causation

Associations

What are interventions?

What are counterfactuals?

Extra – is all machine learning causally the same?

Wrapping it up

References

4 Chapter 3: Regression, Observations, and Interventions


Chapter 3: Regression, Observations, and Interventions

Starting simple – observational data and linear regression

Should we always control for all available covariates?

Regression and structural models

Wrapping it up

References

5 Chapter 4: Graphical Models


Chapter 4: Graphical Models

Graphs, graphs, graphs

What is a graphical model?

DAG your pardon? Directed acyclic graphs in the causal wonderland

Sources of causal graphs in the real world

Extra – is there causality beyond DAGs?

Wrapping it up

References

6 Chapter 5: Forks, Chains, and Immoralities


Chapter 5: Forks, Chains, and Immoralities

Graphs and distributions and how to map between them

Chains, forks, and colliders or…immoralities

Forks, chains, colliders, and regression

Wrapping it up

References

7 Part 2: Causal Inference


Part 2: Causal Inference

8 Chapter 6: Nodes, Edges, and Statistical (In)dependence


Chapter 6: Nodes, Edges, and Statistical (In)dependence

You’re gonna keep ‘em d-separated

Estimand first!

The back-door criterion

The front-door criterion

Are there other criteria out there? Let’s do-calculus!

Wrapping it up

Answer

References

9 Chapter 7: The Four-Step Process of Causal Inference


Chapter 7: The Four-Step Process of Causal Inference

Introduction to DoWhy and EconML

Step 1 – modeling the problem

Step 2 – identifying the estimand(s)

Step 3 – obtaining estimates

Step 4 – where’s my validation set? Refutation tests

Full example

Wrapping it up

References

10 Chapter 8: Causal Models – Assumptions and Challenges


Chapter 8: Causal Models – Assumptions and Challenges

I am the king of the world! But am I?

Positivity

Exchangeability

…and more

Call me names – spurious relationships in the wild

Wrapping it up

References

11 Chapter 9: Causal Inference and Machine Learning – from Matching to
Meta-Learners


Chapter 9: Causal Inference and Machine Learning – from Matching to
Meta-Learners

The basics I – matching

The basics II – propensity scores

Inverse probability weighting (IPW)

S-Learner – the Lone Ranger

T-Learner – together we can do more

X-Learner – a step further

Wrapping it up

References

12 Chapter 10: Causal Inference and Machine Learning – Advanced Estimators,
Experiments, Evaluations, and More


Chapter 10: Causal Inference and Machine Learning – Advanced Estimators,
Experiments, Evaluations, and More

Doubly robust methods – let’s get more!

If machine learning is cool, how about double machine learning?

Causal Forests and more

Heterogeneous treatment effects with experimental data – the uplift odyssey

Extra – counterfactual explanations

Wrapping it up

References

13 Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and
Beyond


Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and
Beyond

Going deeper – deep learning for heterogeneous treatment effects

Transformers and causal inference

Causality and time series – when an econometrician goes Bayesian

Wrapping it up

References

14 Part 3: Causal Discovery


Part 3: Causal Discovery

15 Chapter 12: Can I Have a Causal Graph, Please?


Chapter 12: Can I Have a Causal Graph, Please?

Sources of causal knowledge

Scientific insights

Personal experience and domain knowledge

Causal structure learning

Wrapping it up

References

16 Chapter 13: Causal Discovery and Machine Learning – from Assumptions to
Applications


Chapter 13: Causal Discovery and Machine Learning – from Assumptions to
Applications

Causal discovery – assumptions refresher

The four (and a half) families

Introduction to gCastle

Constraint-based causal discovery

Score-based causal discovery

Functional causal discovery

Gradient-based causal discovery

Encoding expert knowledge

Wrapping it up

References

17 Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning
and Beyond


Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning and
Beyond

Advanced causal discovery with deep learning

Causal discovery under hidden confounding

Extra – going beyond observations

Causal discovery – real-world applications, challenges, and open problems

Wrapping it up!

References

18 Chapter 15: Epilogue


Chapter 15: Epilogue

What we’ve learned in this book

Five steps to get the best out of your causal project

Causality and business

Toward the future of causal ML

Learning causality

Let’s stay in touch

Wrapping it up

References

19 Index


Index

Why subscribe?

20 Other Books You May Enjoy


Other Books You May Enjoy

Packt is searching for authors like you

Share Your Thoughts

Download a free PDF copy of this book

Read table of contents
Previous

CAUSAL INFERENCE AND DISCOVERY IN PYTHON

ALEKSANDER MOLAK

May-23
5

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Feb-22

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MACHINE LEARNING ENGINEERING WITH PYTHON

ANDREW P MCMAHON

Aug-23

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Nov-22

LAYERED DESIGN FOR RUBY ON RAILS APPLICATIONS

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Aug-23

JAVASCRIPT FROM BEGINNER TO PROFESSIONAL

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MICROSERVICES WITH SPRING BOOT 3 AND SPRING CLOUD

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Aug-23

GODOT 4 GAME DEVELOPMENT PROJECTS

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Aug-23
5

SOLUTIONS ARCHITECT’S HANDBOOK

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Jan-22

LEARN REACT WITH TYPESCRIPT

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