Composable Markov processes

By T. Schweder. 1970

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Causal Models for Dynamical Systems (2020, neutral): “For speciﬁc model classes, there is interesting work exploiting the concept of local independence [Schweder, 1970, Mogensen et al., 2018, Mogensen and Hansen, 2019, Didelez, 2000, 2008], with several questions still being open.”
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].”
Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms (2014, background): “An important concept which has recently received increasing attention is that of local independence.(3,4,14) Local independence is a concept that looks at how well future values of a process can be predicted by past.”
Causality, mediation and time: a dynamic viewpoint (2012, methodology): “To obtain a clear understanding of the intuitive content of local independence one may consult references focusing on simple Markov models like Schweder (1970) and Aalen et al. (1980), the latter analysing a medical data set concerning whether the menopause influences the risk of the skin disease…”
A martingale approach to continuous-time marginal structural models (2009, unknown): “t the past of Y does not contribute to a better prediction of the short-term behavior of X. In the setting of event history analysis, this concept has been studied thoroughly by Didelez [6]. Schweder [18] used this concept in a study of composable Markov processes. Aalen et al. made use of local ind”
Effective connectivity: Influence, causality and biophysical modeling (2011, background): “A review and a toolbox for these measures can be found in Seth (2009).”
Causal Learning for Partially Observed Stochastic Dynamical Systems (2018, background): “Local independence was introduced by Schweder (1970) and is a formalization of how the present of one stochastic process depends on the past of others in a dynamical system.”
Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data (2016, methodology): “This was proved by Schweder (1970), and with the formulation provided by Lemma 1 it is evident.”
Markov equivalence of marginalized local independence graphs (2018, methodology): “The notion of local independence was introduced for composable Markov processes by Schweder [37] who also gave examples of graphs describing local independence structures.”
What can Statistics Contribute to a Causal Understanding? (2007, methodology): “As already indicated in diagrams in Schweder (1970) the local dependence concept may be used to show direct and indirect effects.”