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Observation and Experiment An Introduction to Causal Inference

? Observation and Experiment is an introduction to causal inference by one of the field's leading scholars. An award-winning professor at Wharton, Paul;

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Observation and Experiment

investigators confidently claim it raises that risk. How should we study such questions? Observation and Experiment is an introduction to causal;

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Epidemiology by Design

introduction to epidemiology is complete without extensive discussion of causal inference; what's missing is a textbook that takes such an approach;

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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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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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Agent-based Models and Causal Inference

-based Models and Causal Inference is an important and much-needed contribution to sociology and computational social science. The book provides;

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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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Causality and Probability in the Sciences

its careful analysis, "Causality and Probability in the Sciences" heralds the transition of causal inference from an art to a science.;

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Causal Inference: The Mixtape

An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences Causation versus;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal;

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Big Data in Omics and Imaging: Integrated Analysis and Causal Inference

etiology and causal chain of mechanism underlying complex diseases remains elusive. It is time to bring big data, machine learning and causal;

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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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Multimethod Research, Causal Mechanisms, and Case Studies

An innovative and accessible textbook on multimethod and case-study research Multimethod research has become indispensable to doing social;

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Multimethod Research, Causal Mechanisms, and Case Studies

An innovative and accessible textbook on multimethod and case-study research Multimethod research has become indispensable to doing social;

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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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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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An Introduction to Bayesian Inference and Decision

An Introduction to Bayesian Inference and Decision is een boek van Robert L Winkler;

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An Introduction to Bayesian Inference in Econometrics

volume is an early introduction to Bayesian inference and methodology which still has lasting value for today's statistician and student. The;

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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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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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Causation, Evidence, and Inference

trials, statistical and econometric tools, causal modeling and thought experiments. But how do these different methods relate to each other and;

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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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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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