Book Image

Causal Inference and Discovery in Python

By : Aleksander Molak
4.7 (9)
Book Image

Causal Inference and Discovery in Python

4.7 (9)
By: Aleksander Molak

Overview of this book

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. 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.
Table of Contents (21 chapters)
1
Part 1: Causality – an Introduction
7
Part 2: Causal Inference
14
Part 3: Causal Discovery

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

abduction 31

action recommendation systems 261

Active Bayesian Causal Inference (ABCI) 393

acyclic graph

versus cyclic graph 56, 57

adaptive hyper-box matching (AHB) 186

Additive Noise Model (ANM) 148, 349-353, 392

adjacency matrix 58-60, 332

Area Under the Uplift Curve (AUUC) 257

assignment operator 20

association 16, 18, 20

best practice 20-23

associational relationships 18

associative learning 5

assumptions 74, 77, 78

for causal discovery 76, 77

attention mechanism 295

augmented Lagrangian optimizer 374

availability heuristic 322

average treatment effect (ATE) 173, 219, 296, 387

versus average treatment effect on the control (ATC) 175

average treatment effect on the control (ATC) 173

versus average treatment effect (ATE) 175...