scholarly journals Effects of Causes and Causes of Effects

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
A. Philip Dawid ◽  
Monica Musio
Keyword(s):  
Individual Case ◽  
Causal Link ◽  
Structural Equations ◽  
Potential Outcomes ◽  
Annual Review ◽  
Publication Date ◽  
Theoretic Approach ◽  
Additional Structure ◽  
Decision Theoretic Approach ◽  
Application Volume

We describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention (effects of causes) and studying whether there is a causal link between the observed exposure and outcome in an individual case (causes of effects). For each of these, we introduce and compare various formal frameworks that have been proposed for that purpose, including the decision-theoretic approach, structural equations, structural and stochastic causal models, and potential outcomes. We argue that counterfactual concepts are unnecessary for studying effects of causes but are needed for analyzing causes of effects. They are, however, subject to a degree of arbitrariness, which can be reduced, though not in general eliminated, by taking account of additional structure in the problem. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Value of Information Analysis in Models to Inform Health Policy

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Christopher H. Jackson ◽  
Gianluca Baio ◽  
Anna Heath ◽  
Mark Strong ◽  
Nicky J. Welton ◽  
...  
Keyword(s):  
Value Of Information ◽  
Evidence Synthesis ◽  
Annual Review ◽  
Publication Date ◽  
Theoretic Approach ◽  
Information Analysis ◽  
Decision Theoretic Approach ◽  
Expected Benefits ◽  
Different Sources ◽  
Broad Overview

Value of information (VoI) is a decision-theoretic approach to estimating the expected benefits from collecting further information of different kinds, in scientific problems based on combining one or more sources of data. VoI methods can assess the sensitivity of models to different sources of uncertainty and help to set priorities for further data collection. They have been widely applied in healthcare policy making, but the ideas are general to a range of evidence synthesis and decision problems. This article gives a broad overview of VoI methods, explaining the principles behind them, the range of problems that can be tackled with them, and how they can be implemented, and discusses the ongoing challenges in the area. Expected final online publication date for the Annual Review of Statistics, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Simulation and Analysis Methods for Stochastic Compartmental Epidemic Models

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Tapiwa Ganyani ◽  
Christel Faes ◽  
Niel Hens
Keyword(s):  
Stochastic Models ◽  
Online Publication ◽  
Real Life ◽  
Likelihood Estimation ◽  
Annual Review ◽  
Publication Date ◽  
Data Set ◽  
Incidence Data ◽  
Mixed Populations ◽  
Application Volume

This article considers simulation and analysis of incidence data using stochastic compartmental models in well-mixed populations. Several simulation approaches are described and compared. Thereafter, we provide an overview of likelihood estimation for stochastic models. We apply one such method to a real-life outbreak data set and compare models assuming different kinds of stochasticity. We also give references for other publications where detailed information on this topic can be found. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Framing Causal Questions in Life Course Epidemiology

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Bianca L. De Stavola ◽  
Moritz Herle ◽  
Andrew Pickles
Keyword(s):  
Measurement Error ◽  
Life Course ◽  
Online Publication ◽  
Counterfactual Thinking ◽  
Causal Effects ◽  
Annual Review ◽  
Publication Date ◽  
Common Life ◽  
Cohort Data ◽  
Application Volume

We describe the principles of counterfactual thinking in providing more precise definitions of causal effects and some of the implications of this work for the way in which causal questions in life course research are framed and evidence evaluated. Terminology is explained and examples of common life course analyses are discussed that focus on the timing of exposures, the mediation of their effects, observed and unobserved confounders, and measurement error. The examples are illustrated by analyses using singleton and twin cohort data. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Score-Driven Time Series Models

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrew C. Harvey
Keyword(s):  
Time Series ◽  
Circular Data ◽  
Annual Review ◽  
Time Series Models ◽  
Publication Date ◽  
Time Series Modeling ◽  
Dynamic Correlation ◽  
Switching Regimes ◽  
Wide Range ◽  
Application Volume

The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range of disciplines. Their theoretical and practical advantages over other methods are highlighted. Topics covered include robust time series modeling, conditional heteroscedasticity, count data, dynamic correlation and association, censoring, circular data, and switching regimes. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Flexible Models for Complex Data with Applications

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Christophe Ley ◽  
Slađana Babić ◽  
Domien Craens
Keyword(s):  
Online Publication ◽  
Probability Distributions ◽  
Building Blocks ◽  
Circular Data ◽  
Annual Review ◽  
Publication Date ◽  
Complex Data ◽  
Heavy Tailed ◽  
Application Volume ◽  
Flexible Models

Probability distributions are the building blocks of statistical modeling and inference. It is therefore of the utmost importance to know which distribution to use in what circumstances, as wrong choices will inevitably entail a biased analysis. In this article, we focus on circumstances involving complex data and describe the most popular flexible models for these settings. We focus on the following complex data: multivariate skew and heavy-tailed data, circular data, toroidal data, and cylindrical data. We illustrate the strength of flexible models on the basis of concrete examples and discuss major applications and challenges. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Post-selection Inference

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Arun K. Kuchibhotla ◽  
John E. Kolassa ◽  
Todd A. Kuffner
Keyword(s):  
Variable Selection ◽  
Online Publication ◽  
Data Exploration ◽  
Annual Review ◽  
Publication Date ◽  
Simultaneous Inference ◽  
Advantages And Disadvantages ◽  
Application Volume ◽  
Selective Inference

We discuss inference after data exploration, with a particular focus on inference after model or variable selection. We review three popular approaches to this problem: sample splitting, simultaneous inference, and conditional selective inference. We explain how each approach works and highlight its advantages and disadvantages. We also provide an illustration of these post-selection inference approaches. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Online Learning Algorithms

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Nicolò Cesa-Bianchi ◽  
Francesco Orabona
Keyword(s):  
Online Learning ◽  
Convex Optimization ◽  
Online Algorithms ◽  
Analysis Of Algorithms ◽  
Annual Review ◽  
Publication Date ◽  
Sequential Data ◽  
Theoretic Approach ◽  
Mirror Descent ◽  
Information Theoretic Approach

Online learning is a framework for the design and analysis of algorithms that build predictive models by processing data one at the time. Besides being computationally efficient, online algorithms enjoy theoretical performance guarantees that do not rely on statistical assumptions on the data source. In this review, we describe some of the most important algorithmic ideas behind online learning and explain the main mathematical tools for their analysis. Our reference framework is online convex optimization, a sequential version of convex optimization within which most online algorithms are formulated. More specifically, we provide an in-depth description of online mirror descent and follow the regularized leader, two of the most fundamental algorithms in online learning. As the tuning of parameters is a typically difficult task in sequential data analysis, in the last part of the review we focus on coin-betting, an information-theoretic approach to the design of parameter-free online algorithms with good theoretical guarantees. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Causality and Information Transfer Between the Solar Wind and the Magnetosphere–Ionosphere System

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 390
Author(s):  
Pouya Manshour ◽  
Georgios Balasis ◽  
Giuseppe Consolini ◽  
Constantinos Papadimitriou ◽  
Milan Paluš
Keyword(s):  
Solar Wind ◽  
Information Transfer ◽  
Linear Time ◽  
Vertical Component ◽  
Causal Link ◽  
Theoretic Approach ◽  
Geomagnetic Disturbances ◽  
Magnetospheric Substorms ◽  
Solar Wind Parameters ◽  
Information Theoretic Approach

An information-theoretic approach for detecting causality and information transfer is used to identify interactions of solar activity and interplanetary medium conditions with the Earth’s magnetosphere–ionosphere systems. A causal information transfer from the solar wind parameters to geomagnetic indices is detected. The vertical component of the interplanetary magnetic field (Bz) influences the auroral electrojet (AE) index with an information transfer delay of 10 min and the geomagnetic disturbances at mid-latitudes measured by the symmetric field in the H component (SYM-H) index with a delay of about 30 min. Using a properly conditioned causality measure, no causal link between AE and SYM-H, or between magnetospheric substorms and magnetic storms can be detected. The observed causal relations can be described as linear time-delayed information transfer.

Endogenous Retroviruses Drive Resistance and Promotion of Exogenous Retroviral Homologs

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Elliott S. Chiu ◽  
Sue VandeWoude
Keyword(s):  
Immune Modulation ◽  
Viral Infections ◽  
Viral Evolution ◽  
Endogenous Retroviruses ◽  
Annual Review ◽  
Publication Date ◽  
Biological Functions ◽  
Self Tolerance ◽  
Vertebrate Hosts ◽  
Insight Into

Endogenous retroviruses (ERVs) serve as markers of ancient viral infections and provide invaluable insight into host and viral evolution. ERVs have been exapted to assist in performing basic biological functions, including placentation, immune modulation, and oncogenesis. A subset of ERVs share high nucleotide similarity to circulating horizontally transmitted exogenous retrovirus (XRV) progenitors. In these cases, ERV–XRV interactions have been documented and include ( a) recombination to result in ERV–XRV chimeras, ( b) ERV induction of immune self-tolerance to XRV antigens, ( c) ERV antigen interference with XRV receptor binding, and ( d) interactions resulting in both enhancement and restriction of XRV infections. Whereas the mechanisms governing recombination and immune self-tolerance have been partially determined, enhancement and restriction of XRV infection are virus specific and only partially understood. This review summarizes interactions between six unique ERV–XRV pairs, highlighting important ERV biological functions and potential evolutionary histories in vertebrate hosts. Expected final online publication date for the Annual Review of Animal Biosciences, Volume 9 is February 16, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A decision theoretic approach to information retrieval

1990 ◽  
Vol 15 (3) ◽  
pp. 311-340 ◽  
Author(s):  
James C. Moore ◽  
William B. Richmond ◽  
Andrew B. Whinston
Keyword(s):  
Information Retrieval ◽  
Theoretic Approach ◽  
Decision Theoretic Approach
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