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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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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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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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Handbook of Graphical Models

, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number;

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Handbook of Graphical Models

, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number;

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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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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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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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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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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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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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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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Extreme Risk Management

causation. Extreme Risk Management brings causation into the equation. The use of causal models in risk management, securities valuation, and;

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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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Assessing the Use of Agent-Based Models for Tobacco Regulation

inherent to tobacco use onset, cessation, and relapse, and given the heterogeneity of those social interactions, agent-based models have the;

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Modeling and Simulation of Complex Systems

Robert Siegfried presents a framework for efficient agent-based modeling and simulation of complex systems. He compares different;

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Sense and Nonsense of Statistical Inference: Controversy

epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as;

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Sense and Nonsense of Statistical Inference

epistemology; reveals fallacious practices in statistical causal inference, stressing the misuse of regression models and time-series analysis as;

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Agent-Based Models of Polarization and Ethnocentrism

this by building agent based models of polarization and ethnocentrism. In the process, we develop: stochastic models, which add a crucial;

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Agent-based Models of the Economy

the power of agent-based models along their methodology, and it provides several examples of applications spanning from public policy;

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Rethinking Macroeconomics with Endogenous Market Structure

interaction of industrial firms and the macroeconomy. This innovative new book develops a bottom-up, agent-based framework that shows how strategic;

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Hierarchical Modeling and Inference in Ecology

.This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models;

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