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The book focuses on using machine learning techniques to uncover cause-and-effect relationships in data, moving beyond mere correlations. From a Pearlian perspective, this involves utilizing causal diagrams (DAGs) and do-calculus to formalize causal reasoning.

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 moreDiscover modern causal inference techniques for average and heterogenous treatment effect estimationExplore 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 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 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.

Previous studies have found that threatening stimuli are more readily perceived and more intensely experienced when presented during cardiac systole compared with diastole. Also, threatening stimuli are judged as physically closer than neutral ones. In a pre-registered study, we tested these effects and their interaction using a naturalistic (interactive and three-dimensional) experimental design in immersive virtual reality: we briefly displayed threatening and non-threatening animals (four each) at varying distances (1.5–5.5 m) to a group of young, healthy participants ( n = 41) while recording their electrocardiograms (ECGs). Participants then pointed to the location where they had seen the animal (approx. 29 000 trials in total). Our pre-registered analyses indicated that perceived distances to both threatening and non-threatening animals did not differ significantly between cardiac phases—with Bayesian analysis supporting the null hypothesis. There was also no evidence for an association between subjective fear and perceived proximity to threatening animals. These results contrast with previous findings that used verbal or declarative distance measures in less naturalistic experimental conditions. Furthermore, our findings suggest that the cardiac phase-related variation in threat processing may not generalize across different paradigms and may be less relevant in naturalistic scenarios than under more abstract experimental conditions.