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.
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Causality : statistical perspectives and applications by Carlo Berzuini. ISBN 9780470665565. Published by Wiley in 2012. Publication and catalogue information, links to buy online and reader comments.
