A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods.

By James M. Robins. 1987

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Causal inference in statistics: An overview (2009, background): “Weeding out myths and misconceptions from outdated traditions (Meek and Glymour, 1994; Greenland et al., 1999; Cole and Hernán, 2002; Arah, 2008; Shrier, 2009; Pearl, 2009b).”
A general identification condition for causal effects (2002, background): “…unmeasured confounders, all causal effects are identifiable, that is, the joint response of any set Y of variables to intervention on a set T of treatment variables, denoted Pt y ,1 can be estimated consistently from nonexperimental data (Robins 1987; Spirtes, Glymour, & Scheines 1993; Pearl 1993).”
Pattern–mixture and selection models for analysing longitudinal data with monotone missing patterns (2003, background): “…t be independent of the entire unobserved future measurements whereas sequential ignorability requires only conditional independence with the outcome of interest Y. Sequential ignorability is equivalent to the assumption of no unmeasured confounders made in causal inference models (Robins, 1987).”
Causal diagrams for epidemiologic research. (1999, background): “7 This effect is the bias that occurs when unmeasured health conditions (U) influence decisions to leave work (for example, quit, take disability leave, or retire) and also influence mortality (D); when this happens, leaving work may become associated with mortality, even if it has no effect on…”
Learning Treatment-Response Models from Multivariate Longitudinal Data (2017, methodology): “the treatment effect) has been studied extensively (Robins, 1987; Robins et al., 2000; Gill and Robins, 2001; Bang and Robins, 2005).”
Identifiability and Exchangeability for Direct and Indirect Effects (1992, unknown): “R1i - Roc - [Il(R11 - R11B) - IoI(Ro1 - RO1B)] (4)”
Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies (2004, background): “…Feldman (1991), Gelman and King (1991), Greenland and Poole (1988), Greenland, Robins, and Pearl (1999), Holland (1988a, 1988b), Kadane and Seidenfeld (1990), Robins (1987, 1989), Rosenbaum (1987), Smith and Sugden (1988), Sobel (1990, 1995, 1996), Sugden (1988), and their references, and so forth.”
Probabilities Of Causation: Three Counterfactual Interpretations And Their Identification (1999, background): “Prominent among these we find the consistency condition (Robins 1987):”
Identification and Inference for Econometric Models: On Specifying Graphical Models for Causation and the Identification Problem (2005, unknown): “Also see Robins (1986, 1987).”