Causal Reasoning from Longitudinal Data *

By Elja Arjas, J. Parner. 2004

77 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, background): “Schulam & Saria (2017) build upon work from Lok et al. (2008); Arjas & Parner (2004) and use marked point processes and Gaussian processes to learn causal effects in continuous-time data.”
Causal inference in statistics: An overview (2009, background): “…also that the truncated factorization formula is not restricted to interventions on a single variable; it is applicable to simultaneous or sequential interventions such as those invoked in the analysis of time varying treatment with time varying confounders (Robins, 1986; Arjas and Parner, 2004).”
Reliable Decision Support using Counterfactual Models (2017, methodology): “Arjas and Parner [2004] develop an alternative framework for causal inference using Bayesian posterior predictive distributions to estimate the effects of actions in continuous time on a final outcome. Both Lok [2008] and Arjas and Parner [2004] use marked point processes to formalize assumptions th”
History of applications of martingales in survival analysis. (2010, unknown): “Connected to this are the work of Arjas and Parner [23] on posterior predictive distributions for marked point process models and the dynamic path analysis of Fosen et al. [46], see also [9].”
What-If Reasoning with Counterfactual Gaussian Processes (2017, methodology): “The CGP builds on previous ideas in continuous-time causal inference (e.g., Robins 1997, Arjas and Parner 2004, Lok 2008), but is unique in that it can predict the full counterfactual trajectory ; we combined marked point processes (MPP) with GPs to model observational traces , and described three…”
3. The Foundations of Causal Inference (2010, background): “…the truncated factorization formula is not restricted to interventions on a single variable; it is applicable to simultaneous or sequential interventions such as those invoked in the analysis of time-varying treatment with time-varying confounders (Pearl and Robins 1995; Arjas and Parner 2004).”
Counterfactual analyses with graphical models based on local independence (2011, background): “It has also been considered in the general context of marked point processes in [3, 4, 8, 20] and [25].”
Graphical models for marked point processes based on local independence (2007, methodology): “For non-graphical approaches to causal reasoning in a continuous time event history setting see Eerola (1994), Lok (2001), Arjas and Parner (2004) and Fosen et al.”
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview (2010, background): “This is essentially a discrete-time version of Definition 2 (ii) of Arjas and Parner (2004), but with the additional vital requirement that the unobservable variables U involved already be such as to allow us to assume the extended stability property (13).”