Causality statistical perspectives and applications /
"This book looks at a broad collection of contributions from experts in their fields"--
I tiakina i:
| Kaituhi rangatōpū: | |
|---|---|
| Ētahi atu kaituhi: | , , |
| Hōputu: | Tāhiko īPukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
Chichester, West Sussex, U.K. :
Wiley,
2012.
|
| Rangatū: | Wiley series in probability and statistics.
|
| Ngā marau: | |
| Urunga tuihono: | An electronic book accessible through the World Wide Web; click to view |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
Rārangi ihirangi:
- Statistical causality : some historical remarks
- The language of potential outcomes
- Structural equations, graphs and interventions
- The decision-theoretic approach to causal
- Causal inference as a prediction problem : assumptions, identification, and evidence synthesis
- Graph-based criteria of identifiability of causal questions
- Causal inference from observational data : a Bayesian predictive approach
- Causal inference from observing sequences of actions
- Causal effects and natural laws : towards a conceptualization of causal counterfactuals
- For non-manipulable exposures, with application to the effects of race and sex
- Cross-classifications by joint potential outcomes
- Estimation of direct and indirect effects
- The mediation formula : a guide to the assessment of causal pathways in nonlinear models
- The sufficient cause framework in statistics, philosophy and the biomedical and social sciences
- Inference about biological mechanism on the basis of epidemiological data
- Ion channels and multiple sclerosis
- Supplementary variables for causal estimation
- Time-varying confounding : some practical considerations in a likelihood framework
- Natural experiments as a means of testing causal inferences
- Nonreactive and purely reactive doses in observational studies
- Evaluation of potential mediators in randomized trials of complex interventions (psychotherapies)
- Causal inference in clinical trials
- Granger causality and causal inference in time series analysis
- Dynamic molecular networks and mechanisms iIn the biosciences : a statistical framework.