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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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Epidemiology by Design

-based medicine, and comparative effectiveness research. Causal inference is the theoretical foundation underlying all of the above. No;

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Counterfactuals and Causal Inference

, the counterfactual model of causality for observational data analysis is presented, and methods for causal effect estimation are demonstrated;

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A Logical Theory of Causality

A general formal theory of causal reasoning as a logical study of causal models, reasoning, and inference.In this book;

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Understanding Counterfactuals, Understanding Causation

been interested in connections between causal and counterfactual claims on the level of meaning or truth-conditions. More recently, however;

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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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Causality and Probability in the Sciences

biology, from econometrics to physics, make use of probability and statistics in order to infer causal relationships. However, the very;

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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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Explaining War and Peace

causal logics of necessary and sufficient conditions, demonstrates the variety of different ways in which necessary condition counterfactuals are;

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Explaining War and Peace

causal logics of necessary and sufficient conditions, demonstrates the variety of different ways in which necessary condition counterfactuals are;

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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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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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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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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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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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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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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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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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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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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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