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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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Statistical Models & Causal Inference

David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the;

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Statistical Models and Causal Inference

David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the;

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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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Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies

generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted;

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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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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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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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Statistical Rethinking

interpretations in your own modeling work. The text presents causal inference and generalized linear multilevel models from a simple Bayesian;

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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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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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Epidemiology

introductory level. Epidemiology: An Introduction closes the gap. It begins with a brief, lucid discussion of casual thinking and causal inference and;

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Causal Inf In Statistics Social

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their;

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Combining Human Genetics and Causal Inference to Understand Human Disease and Development

In human genetics, causal inference methods leverage large omics data sets and phenotypic information to decipher various cause-and-effect;

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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

, Gary Goertz introduces a new integrated approach that unites three corners of a powerful research triad--causal mechanisms, cross-case causal;

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Multimethod Research, Causal Mechanisms, and Case Studies

, Gary Goertz introduces a new integrated approach that unites three corners of a powerful research triad--causal mechanisms, cross-case causal;

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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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Hume's Theory of the Understanding

This book, first published in 1935, is an examination of Hume's theories of causal inference and belief in substance and his analysis;

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Hume's Theory of the Understanding

This book, first published in 1935, is an examination of Hume's theories of causal inference and belief in substance and his analysis;

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