Causal Inference In Python
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Causal Inference In Python
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Author : Matheus Facure
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-07-14
Causal Inference In Python written by Matheus Facure and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-14 with Computers categories.
How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example. With this book, you will: Learn how to use basic concepts of causal inference Frame a business problem as a causal inference problem Understand how bias gets in the way of causal inference Learn how causal effects can differ from person to person Use repeated observations of the same customers across time to adjust for biases Understand how causal effects differ across geographic locations Examine noncompliance bias and effect dilution
Causal Inference And Discovery In Python
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Author : Aleksander Molak
language : en
Publisher:
Release Date : 2023
Causal Inference And Discovery In Python written by Aleksander Molak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Causal Inference And Discovery In Python
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Author : Aleksander Molak
language : en
Publisher:
Release Date : 2023-05-31
Causal Inference And Discovery In Python written by Aleksander Molak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with categories.
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.
Business Case Guide To Causal Inference With Python
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Author : Gareth Thomas
language : en
Publisher: Independently Published
Release Date : 2025-07-10
Business Case Guide To Causal Inference With Python written by Gareth Thomas and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-10 with Business & Economics categories.
Applied Causal Inference
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Author : Uday Kamath
language : en
Publisher: Independently Published
Release Date : 2023-10-06
Applied Causal Inference written by Uday Kamath and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-06 with categories.
Recent advancements in causal inference have made it possible to gain profound insight about our world and the complex systems which operate in it. While industry professionals and academics in every domain ask questions of their data, traditional statistical methods often fall short of providing conclusive answers. This is where causality can help. This book gives readers the tools necessary to use causal inference in applied settings by building from theoretical foundations all the way to hands-on case studies in Python. We wrote this book primarily for the practitioner who knows how to work with data but may not be familiar with causal inference concepts, or how to apply those concepts to real-world problems. Part 1 of the book builds from the basic principles of causal inference to the estimation process and into causal discovery, with accompanying exercises and case studies to reinforce concepts. In Parts 2 and 3, we go deeper into cutting-edge applications of causality in machine learning domains, including computer vision, natural language processing, reinforcement learning, and model fairness. The combination of these focuses makes this book a perfect entrypoint into the world of causality for any machine learning professional.
Causal Inference Based Fault Localization For Python Numerical Programs
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Author : Jiacheng Ding
language : en
Publisher:
Release Date : 2018
Causal Inference Based Fault Localization For Python Numerical Programs written by Jiacheng Ding and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computer engineering categories.
Fault localization is among the most time-consuming processes in software development, faults in numerical programs are difficult to identify. To address this problem, we propose a causal inference based fault localization technique that statistically estimates the causal effect of numerical variables on programs failure occurrence. This work consists of two parts. First, a Static Single Assignment (SSA) form converter is developed to instrument Python source code and facilitate value profiling. Second, we use Random Forest to estimate potential outcomes of multi-level treatment and search the maximum difference to assign program variables suspiciousness scores. In the experiment, we test how our method reacts to various types of faults and show an empirical result that our method outperforms an existed similar method-ESP.
Causal Data Science With Python
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Author : Dorcas O Folarin
language : en
Publisher: Independently Published
Release Date : 2025-10-12
Causal Data Science With Python written by Dorcas O Folarin and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-12 with Mathematics categories.
In the age of big data, correlation is everywhere - but causation is what truly drives understanding and decision-making. Causal Data Science with Python: From Correlation to Decision bridges the gap between predictive modeling and causal reasoning, offering a practical, hands-on guide to uncovering cause-and-effect relationships in data. This book introduces the principles of causal inference and their implementation in Python, combining the rigor of statistics with the flexibility of modern machine learning. Through real-world examples and step-by-step coding exercises, readers learn to move beyond simple associations and make robust causal claims that support confident decisions in business, healthcare, economics, and the social sciences. Key topics include counterfactual reasoning, randomized experiments, propensity score methods, instrumental variables, causal graphs (DAGs), mediation analysis, and machine learning for causal effect estimation. The text balances theory and practice, providing clear explanations of concepts such as the Rubin Causal Model, do-calculus, and Structural Causal Models (SCMs) - alongside Python implementations using libraries such as DoWhy, EconML, CausalML, and PyMC. Whether you are a data scientist seeking to build fairer AI systems, a social scientist analyzing interventions, or a policymaker looking for evidence-based insights, this book offers the tools and reasoning framework to transform data into meaningful, actionable understanding.
Causal Inference With Bayesian Networks
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Author : YOUSRI EL. FATTAH
language : en
Publisher:
Release Date : 2024
Causal Inference With Bayesian Networks written by YOUSRI EL. FATTAH and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Causal Ai
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Author : Robert Osazuwa Ness
language : en
Publisher: Simon and Schuster
Release Date : 2025-03-18
Causal Ai written by Robert Osazuwa Ness and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Computers categories.
Causal AI is a practical introduction to building AI models that can reason about causality. Robert Ness' clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes.
The Effect
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Author : Nick Huntington-Klein
language : en
Publisher: CRC Press
Release Date : 2025-07-09
The Effect written by Nick Huntington-Klein and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-09 with Business & Economics categories.
The Effect: An Introduction to Research Design and Causality, Second edition is an excellent teaching text about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we “add a control variable” what does that actually do? The target audience is practitioners as well as undergraduate and graduate students studying causal inference in various fields such as statistics, econometrics, biostatistics, the social sciences and data science. Key Features: Extensive code examples in R, Stata, and Python Chapters on heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions An easy-to-read conversational tone Up-to-date coverage of methods with fast-moving literatures like difference-in-differences The second edition features a new chapter on partial identification, updated materials, methods, and writing throughout, and additional materials for help navigating the book or in using the book in teaching.