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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

Bayesian approaches to statistical inference based on decision theory Discusses the role of causal analysis in statistical decision theory;

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Statistical Approaches to Causal Analysis

A practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods;

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Statistics & Causality

. Statistics and Causality: Methods for Applied Empirical Research also includes: * New statistical methodologies and approaches to causal analysis;

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Big Data in Omics and Imaging, Two Volume Set

to Big Data in Omic and Imaging: Association Analysis, but can be read independently. Introduce causal inference;

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Direction Dependence in Statistical Modeling

incorporates the latest research for the statistical analysis of hypotheses that are compatible with the causal direction of dependence of variable;

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Quantitative Methods in Criminology

and causal analysis, it will be of interest to anyone concerned with criminology and criminal justice, as well as those with specialized;

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Cause & Correlation In Biology

cause-effect relationships through a series of statistical methods, this book explains how to test causal hypotheses when randomised experiments;

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Statistical Methods for Drug Safety

Data The book describes linear and non-linear mixed-effects models, discrete-time survival models, and new approaches to the meta-analysis;

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Statistical Methods for Drug Safety

of statistical approaches for analyzing pharmacoepidemiologic data. It covers both commonly used techniques, such as proportional reporting ratios for;

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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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Advanced Statistics for Testing Assumed Causal Relationships

mathematical knowledge. It shows multiple regression analysis accurately reconstructs the causal relationships between phenomena. So, it can be used to;

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Propensity Score Analysis: Statistical Methods and Applications

Provides readers with a systematic review of the origins, history, and statistical foundations of Propensity Score Analysis (PSA) and;

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Statistical Models & Causal Inference

epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates;

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Statistical Models and Causal Inference

epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates;

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Statistical Models For Causal Analysis

This volume provides an overview of statistical models commonly used in the causal analyses of non-experimental data in the social and;

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

to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism;

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

to deep causal inference and from genetic analysis alone to integrated omics and imaging data analysis for unraveling the mechanism;

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Causality In A Social World

causal effects, explains and evaluates identification assumptions using application examples, and compares innovative statistical strategies with;

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The Root of All Evil?

analyses that reject that connection. Upon a deeper analysis, however, both of these approaches are oversimplifications. To adequately answer the;

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Event History Analysis With Stata

Nowadays, event history analysis can draw on a well-established set of statistical tools for the description and causal analysis of event;

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Event History Analysis With Stata

Nowadays, event history analysis can draw on a well-established set of statistical tools for the description and causal analysis of event;

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Stochastic Causality

metaphysical views of causality, Bayesian nets and causal dependence, and causation in the special sciences. Some papers stress the statistical;

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Agent-based Models and Causal Inference

provides a powerful and cogent alternative to standard statistical causal-modeling approaches to causation. Especially valuable is Manzo's careful;

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Observation and Experiment An Introduction to Causal Inference

in understanding the basic ideas and approaches to causal inference. -Psychometrika A very valuable contribution...Highly recommended;

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Big Data in Healthcare

Big Data in Healthcare: Statistical Analysis of the Electronic Health Record provides the statistical tools that healthcare leaders need to;

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