A General Dynamical Statistical model with Causal Interpretation

DOI10.1111/j.1467-9868.2009.00703.xzbMath1250.62004arXiv0710.4396OpenAlexW2111958820MaRDI QIDQ2920265

Anne Gégout-Petit, Daniel Commenges

Publication date: 25 October 2012

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0710.4396

zbMATH Keywords

stochastic processesrandomizationdifferential equationscausalitydirected graphshuman immunodeficiency virusdynamical modelscausal influence

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Foundational topics in statistics (62A99)

Related Items

The stochastic system approach for estimating dynamic treatments effect ⋮ Markov equivalence of marginalized local independence graphs ⋮ Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study ⋮ Graphical modeling of stochastic processes driven by correlated noise ⋮ Nonparametric conditional local independence testing ⋮ Causality with finite horizon of the past in continuous time ⋮ Feedback and Mediation in Causal Inference Illustrated by Stochastic Process Models ⋮ Joint Modeling of the Clinical Progression and of the Biomarkers' Dynamics Using a Mechanistic Model ⋮ Compartmental model diagrams as causal representations in relation to DAGs ⋮ Dealing with death when studying disease or physiological marker: the stochastic system approach to causality ⋮ Stochastic kinetic models: dynamic independence, modularity and graphs ⋮ A general definition of influence between stochastic processes ⋮ Evidence synthesis through a degradation model applied to myocardial infarction ⋮ Estimation of Direct Effects for Survival Data by using the Aalen Additive Hazards Model

Uses Software

• invGauss

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Survival and event history analysis. A process point of view
• Graphical models for associations between variables, some of which are qualitative and some quantitative
• Ignorability and coarse data
• Probabilistic causality in longitudinal studies
• Causal inference for complex longitudinal data: the continuous case.
• Uniform consistency in causal inference
• Dynamic modelling and causality
• Identification of Causal Effects Using Instrumental Variables
• Graphical Models for Marked Point Processes Based on Local Independence
• Statistics and Causal Inference
• Causal Inference Without Counterfactuals
• Estimating the causal effect of a time‐varying treatment on time‐to‐event using structural nested failure time models
• On Choosing and Bounding Probability Metrics
• Noncausality in Continuous Time
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• The Bayesian Treatment of Auxiliary Hypotheses: Reply to Fitelson and Waterman
• Identifying Direct and Indirect Effects in a Non-Counterfactual Framework
• Mendelian randomization as an instrumental variable approach to causal inference
• Likelihood for Generally Coarsened Observations from Multistate or Counting Process Models
• What can Statistics Contribute to a Causal Understanding?
• Graphical Models for Composable Finite Markov Processes
• Maximum Likelihood Estimation in Dynamical Models of HIV
• Composable Markov processes

This page was built for publication: A General Dynamical Statistical model with Causal Interpretation