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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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Multimethod Research, Causal Mechanisms, and Case Studies

are the central goals of multimethod and case study research, and provides a logic for connecting case studies and causal mechanism analysis;

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Multimethod Research, Causal Mechanisms, and Case Studies

are the central goals of multimethod and case study research, and provides a logic for connecting case studies and causal mechanism analysis;

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Multi-Method Social Science

of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key;

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Multi-method Social Science

of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key;

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

association analysis and modern causation analysis Use combinatorial optimization methods and various causal models as a general framework;

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

various causal models as a general framework for inferring multilevel omic and image causal networks Present statistical methods;

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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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Big Data in Omics and Imaging, Two Volume Set

examples and case studies Will have an accompanying website with R code Provides a natural extension and companion volume;

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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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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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Designing Social Inquiry

the most out of qualitative research. It addresses topics such as interpretation and inference, comparative case studies, constructing causal;

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Designing Social Inquiry

the most out of qualitative research. It addresses topics such as interpretation and inference, comparative case studies, constructing causal;

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Bayesian Artificial Intelligence

case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian networks Illustrated with real;

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Bayesian Artificial Intelligence

errors New applications and case studies More coverage on the uses of causal interventions to understand and reason with causal Bayesian;

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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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Causal Inf In Statistics Social

assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading;

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Extreme Risk Management

Measurement, and Portfolio Management Using Causal Models Risk Fusion and Super Models: A Framework for Enterprise Risk Management Inferring Causality;

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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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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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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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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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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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Basic Principles And Practical Applications In Epidemiological Research

simplifies the design and inferences of human observational studies into two types: descriptive and causal. It clarifies how and why causal inference;

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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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