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Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook Key Features 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 Book Description Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefit Who this book is for This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning. Table of Contents Causality – Hey, We Have Machine Learning, So Why Even Bother? Judea Pearl and the Ladder of Causation Regression, Observations, and Interventions Graphical Models Forks, Chains, and Immoralities Nodes, Edges, and Statistical (In)dependence The Four-Step Process of Causal Inference Causal Models – Assumptions and Challenges Causal Inference and Machine Learning – from Matching to Meta-Learners Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond Can I Have a Causal Graph, Please? Causal Discovery and Machine Learning – from Assumptions to Applications Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond Epilogue Cover Title Page Copyright and Credit Dedicated Foreword Contributors Acknowledgments Table of Contents Preface Part 1: Causality – an Introduction Chapter 1: Causality – Hey, We Have Machine Learning, So Why Even Bother? A brief history of causality Why causality? Ask babies! Interacting with the world Confounding – relationships that are not real How not to lose money... and human lives A marketer’s dilemma Let’s play doctor! Associations in the wild Wrapping it up References Chapter 2: Judea Pearl and the Ladder of Causation From associations to logic and imagination – the Ladder of Causation Associations Let’s practice! What are interventions? Changing the world Correlation and causation What are counterfactuals? Let’s get weird (but formal)! The fundamental problem of causal inference Computing counterfactuals Time to code! Extra – is all machine learning causally the same? Causality and reinforcement learning Causality and semi-supervised and unsupervised learning Wrapping it up References Chapter 3: Regression, Observations, and Interventions Starting simple – observational data and linear regression Linear regression p-values and statistical significance Geometric interpretation of linear regression Reversing the order Should we always control for all available covariates? Navigating the maze If you don’t know where you’re going, you might end up somewhere else Get involved! To control or not to control? Regression and structural models SCMs Linear regression versus SCMs Finding the link Regression and causal effects Wrapping it up References Chapter 4: Graphical Models Graphs, graphs, graphs Types of graphs Graph representations Graphs in Python What is a graphical model? DAG your pardon? Directed acyclic graphs in the causal wonderland Definitions of causality DAGs and causality Let’s get formal! Limitations of DAGs Sources of causal graphs in the real world Causal discovery Expert knowledge Combining causal discovery and expert knowledge Extra – is there causality beyond DAGs? Dynamical systems Cyclic SCMs Wrapping it up References Chapter 5: Forks, Chains, and Immoralities Graphs and distributions and how to map between them How to talk about independence Choosing the right direction Conditions and assumptions Chains, forks, and colliders or...immoralities A chain of events Chains Forks Colliders, immoralities, or v-structures Ambiguous cases Forks, chains, colliders, and regression Generating the chain dataset Generating the fork dataset Generating the collider dataset Fitting the regression models Wrapping it up References Part 2: Causal Inference Chapter 6: Nodes, Edges, and Statistical (In)dependence You’re gonna keep ‘em d-separated Practice makes perfect – d-separation Estimand first! We live in a world of estimators So, what is an estimand? The back-door criterion What is the back-door criterion? Back-door and equivalent estimands The front-door criterion Can GPS lead us astray? London cabbies and the magic pebble Opening the front door Three simple steps toward the front door Front-door in practice Are there other criteria out there? Let’s do-calculus! The three rules of do-calculus Instrumental variables Wrapping it up Answer References Chapter 7: The Four-Step Process of Causal Inference Introduction to DoWhy and EconML Python causal ecosystem Why DoWhy? Oui, mon ami, but what is DoWhy? How about EconML? Step 1 – modeling the problem Creating the graph Building a CausalModel object Step 2 – identifying the estimand(s) Step 3 – obtaining estimates Step 4 – where’s my validation set? Refutation tests How to validate causal models Introduction to refutation tests Full example Step 1 – encode the assumptions Step 2 – getting the estimand Step 3 – estimate! Step 4 – refute them! Wrapping it up References Chapter 8: Causal Models – Assumptions and Challenges I am the king of the world! But am I? In between Identifiability Lack of causal graphs Not enough data Unverifiable assumptions An elephant in the room – hopeful or hopeless? Let’s eat the elephant Positivity Exchangeability Exchangeable subjects Exchangeability versus confounding ...and more Modularity SUTVA Consistency Call me names – spurious relationships in the wild Names, names, names Should I ask you or someone who’s not here? DAG them! More selection bias Wrapping it up References Chapter 9: Causal Inference and Machine Learning – from Matching to Meta-Learners The basics I – matching Types of matching Treatment effects – ATE versus ATT/ATC Matching estimators Implementing matching The basics II – propensity scores Matching in the wild Reducing the dimensionality with propensity scores Propensity score matching (PSM) Inverse probability weighting (IPW) Many faces of propensity scores Formalizing IPW Implementing IPW IPW – practical considerations S-Learner – the Lone Ranger The devil’s in the detail Mom, Dad, meet CATE Jokes aside, say hi to the heterogeneous crowd Waving the assumptions flag You’re the only one – modeling with S-Learner Small data S-Learner’s vulnerabilities T-Learner – together we can do more Forcing the split on treatment T-Learner in four steps and a formula Implementing T-Learner X-Learner – a step further Squeezing the lemon Reconstructing the X-Learner X-Learner – an alternative formulation Implementing X-Learner Wrapping it up References Chapter 10: Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More Doubly robust methods – let’s get more! Do we need another thing? Doubly robust is not equal to bulletproof... ...but it can bring a lot of value The secret doubly robust sauce Doubly robust estimator versus assumptions DR-Learner – crossing the chasm DR-Learners – more options Targeted maximum likelihood estimator If machine learning is cool, how about double machine learning? Why DML and what’s so double about it? DML with DoWhy and EconML Hyperparameter tuning with DoWhy and EconML Is DML a golden bullet? Doubly robust versus DML What’s in it for me? Causal Forests and more Causal trees Forests overflow Advantages of Causal Forests Causal Forest with DoWhy and EconML Heterogeneous treatment effects with experimental data – the uplift odyssey The data Choosing the framework We don’t know half of the story Kevin’s challenge Opening the toolbox Uplift models and performance Other metrics for continuous outcomes with multiple treatments Confidence intervals Kevin’s challenge’s winning submission When should we use CATE estimators for experimental data? Model selection – a simplified guide Extra – counterfactual explanations Bad faith or tech that does not know? Wrapping it up References Chapter 11: Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond Going deeper – deep learning for heterogeneous treatment effects CATE goes deeper SNet Transformers and causal inference The theory of meaning in five paragraphs Making computers understand language From philosophy to Python code LLMs and causality The three scenarios CausalBert Causality and time series – when an econometrician goes Bayesian Quasi-experiments Twitter acquisition and our googling patterns The logic of synthetic controls A visual introduction to the logic of synthetic controls Starting with the data Synthetic controls in code Challenges Wrapping it up References Part 3: Causal Discovery Chapter 12: Can I Have a Causal Graph, Please? Sources of causal knowledge You and I, oversaturated The power of a surprise Scientific insights The logic of science Hypotheses are a species One logic, many ways Controlled experiments Randomized controlled trials (RCTs) From experiments to graphs Simulations Personal experience and domain knowledge Personal experiences Domain knowledge Causal structure learning Wrapping it up References Chapter 13: Causal Discovery and Machine Learning – from Assumptions to Applications Causal discovery – assumptions refresher Gearing up Always trying to be faithful... ...but it’s difficult sometimes Minimalism is a virtue The four (and a half) families The four streams Introduction to gCastle Hello, gCastle! Synthetic data in gCastle Fitting your first causal discovery model Visualizing the model Model evaluation metrics Constraint-based causal discovery Constraints and independence Leveraging the independence structure to recover the graph PC algorithm – hidden challenges PC algorithm for categorical data Score-based causal discovery Tabula rasa – starting fresh GES – scoring GES in gCastle Functional causal discovery The blessings of asymmetry ANM model Assessing independence LiNGAM time Gradient-based causal discovery What exactly is so gradient about you? Shed no tears GOLEMs don’t cry The comparison Encoding expert knowledge What is expert knowledge? Expert knowledge in gCastle Wrapping it up References Chapter 14: Causal Discovery and Machine Learning – Advanced Deep Learning and Beyond Advanced causal discovery with deep learning From generative models to causality Looking back to learn who you are DECI’s internal building blocks DECI in code DECI is end-to-end Causal discovery under hidden confounding The FCI algorithm Other approaches to confounded data Extra – going beyond observations ENCO ABCI Causal discovery – real-world applications, challenges, and open problems Wrapping it up! References Chapter 15: Epilogue What we’ve learned in this book Five steps to get the best out of your causal project Starting with a question Obtaining expert knowledge Generating hypothetical graph(s) Check identifiability Falsifying hypotheses Causality and business How causal doers go from vision to implementation Toward the future of causal ML Where are we now and where are we heading? Causal benchmarks Causal data fusion Intervening agents Causal structure learning Imitation learning Learning causality Let’s stay in touch Wrapping it up References Index Other Books You May Enjoy Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesExamine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and moreDiscover modern causal inference techniques for average and heterogenous treatment effect estimationExplore and leverage traditional and modern causal discovery methodsBook DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You'll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you'll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you'll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You'll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.What you will learnMaster the fundamental concepts of causal inferenceDecipher the mysteries of structural causal modelsUnleash the power of the 4-step causal inference process in PythonExplore advanced uplift modeling techniquesUnlock the secrets of modern causal discovery using PythonUse causal inference for social impact and community benefitWho this book is forThis book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who've worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.