Causality, mediation and time: a dynamic viewpoint
By O. Aalen, K. Røysland, J. M. Gran, B. Ledergerber. 2012
103 citations recorded by the source corpus.
Summary. Time dynamics are often ignored in causal modelling. Clearly, causality must operate in time and we show how this corresponds to a mechanistic, or system, understanding of causality. The established counterfactual definitions of direct and indirect effects depend on an ability to manipulate the mediator which may not hold in practice, and we argue that a mechanistic view may be better. Graphical representations based on local independence graphs and dynamic path analysis are used to facilitate communication as well as providing an overview of the dynamic relations ‘at a glance’. The relationship between causality as understood in a mechanistic and in an interventionist sense is discussed. An example using data from the Swiss HIV Cohort Study is presented.
Subfields: Statistics and Probability
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In Statistics and Probability
- Søren Wengel Mogensen
- Po-Ling Loh
- Niels Richard Hansen
- Peter Buhlmann
- Alexander Sokol
- K. Røysland
- Martin J. Wainwright
- Sara A. van de Geer
Citations in the corpus
- Causal interpretation of stochastic differential equations (2013, background): “1 of [1] it is noted that both ordinary differential equations and stochastic differential equations (SDEs) allow for a natural interpretation in terms of “influence”, and that interventions may be defined by substitutions in the differential equations.”
- High-dimensional learning of linear causal networks via inverse covariance estimation (2013, background): “Causal networks arise in a wide variety of applications, including genetics, epidemiology, and time series analysis (Hughes et al., 2000; Stekhoven et al., 2012; Aalen et al., 2012).”
- Markov equivalence of marginalized local independence graphs (2018, methodology): “Several authors have since then used graphs to represent local independence structures of multivariate stochastic process models, in particular for point process models; see, for example, [4, 11–13 , 35].”
- Counterfactual analyses with graphical models based on local independence (2011, background): “Note that local independence graphs are also refered to as local independence graphs (see [1, 9]) and were introduced in [26].”
- Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data (2016, background): “Aalen et al. (2012) discusses Schweder’s theory from a causality and martingale point of view. Gourieroux et al. (1987) have the same approach to causality as we do; they allow general state spaces but are limited to discrete time.”