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Causal Inference: The Mixtape
correlation has been the basis of arguments-economic and otherwise-since the beginning of time. Causal Inference: The Mixtape uses legit real-world;
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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference
Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic;
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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference
Big Data in Omics and Imaging: Integrated Analysis and Causal Inference addresses the recent development of integrated genomic, epigenomic;
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Epidemiology by Design
A (LONG OVERDUE) CAUSAL APPROACH TO INTRODUCTORY EPIDEMIOLOGY Epidemiology is recognized as the science of public health, evidence;
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Causality and Probability in the Sciences
Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to;
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Agent-based Models and Causal Inference
Agent-based Models and Causal Inference Scholars of causal inference have given little credence to the possibility that ABMs could be;
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Counterfactuals and Causal Inference
In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the;
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Counterfactuals & Causal Inference
In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the;
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Confounding in Causal Inference
Causal inference is an important but controversial topic in the social sciences in that it is difficult to statistically control for all;
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Case Studies and Causal Inference: An Integrative Framework
A discussion of the case study method which develops an integrative framework for causal inference in small-n research. This framework;
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Elements of Causal Inference
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The;
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Causation in Population Health Informatics and Data Science
Marketing text: This book covers the overlap between informatics, computer science, philosophy of causation, and causal inference;
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Causal Inf In Statistics Social
treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference;
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Causation, Evidence, and Inference
In this book, Reiss argues in favor of a tight fit between evidence, concept and purpose in our causal investigations in the sciences;
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Multimethod Research, Causal Mechanisms, and Case Studies
inference, and within-case causal inference. He explains how the investigation of causal mechanisms and the making of within-case causal inference;
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Multimethod Research, Causal Mechanisms, and Case Studies
inference, and within-case causal inference. He explains how the investigation of causal mechanisms and the making of within-case causal inference;
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Combining Human Genetics and Causal Inference to Understand Human Disease and Development
to enhance our understanding of human development and disease. The contributors discuss family-based study designs for causal inference;
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Introduction to Statistical Decision Theory
Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision;
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Introduction to Statistical Decision Theory
Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision;
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Ecological Inference
Explores the utility of techniques designed to make the inferences in causal modeling more reliable, including a comparison between;
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The SAGE Handbook of Regression Analysis and Causal Inference
longitudinal data with an emphasis on causal analysis and provides readers with an introduction to and exploration of a large range of techniques.;
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Inference and Intervention
Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications;
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Inference and Intervention
Ryall and Bramson's Inference and Intervention is the first textbook on causal modeling with Bayesian networks for business applications;
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Handbook of Graphical Models
A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models;
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Handbook of Graphical Models
A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models;
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