Causal Inference from Complex Longitudinal Data

By James M. Robins. 1997

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, background): “Causal inference for longitudinal studies has been studied extensively, too [e.g. Vanderweele, 2015, Aalen et al., 2008, Robins, 1997], where the results are often formulated in the language of potential outcomes [Imbens and Rubin, 2015], rather than SCMs.”
Precision Medicine. (2019, background): “Early seminal work in estimating dynamic treatment regimes includes (Robins 1986), (Robins 1997), (Murphy et al 2001), (van der Laan et al 2001), (Murphy 2003), and (Robins 2004).”
Association between nonsteroidal anti-inflammatory drugs and upper gastrointestinal tract bleeding/perforation: an overview of epidemiologic studies published in the 1990s. (2000, unknown): “Second, correct adjustment for time-dependent confounders requires advanced analytical methods not yet widely used.(36) Although identified studies agree on an increased risk of UGIB associated with NSAIDs, the size of the reported risks varies.”
Demystifying Optimal Dynamic Treatment Regimes (2007, unknown): “Robins (1986, 1994, 1997) pioneered the ﬁeld of dynamic treatment regimes.”
The explanation game: a formal framework for interpretable machine learning (2020, unknown): “While several overlapping formulations have been proposed for such adjustments (Galles & Pearl, 1995; Pearl, 1995; Robins, 1997), we follow Tian & Pearl (2002), who provide a provably sound and complete set of causal identifiability conditions for semi-Markovian models (Huang & Valtorta, 2008).”
On the multiply robust estimation of the mean of the g-functional (2017, unknown): “3.2 Iterated conditional mean representation Robins (1986, 1997) derived another representation of θ (g) in the form of an iterated conditional expectation. ηk ( ak, lk; gk ) ≡ ∫ ψ ( LK+1 ) K∏ j=k+1 h∗j ( aj |lj, aj−1 ) K∏ j=k gj ( lj+1|lj , aj ) { K+1∏ j=k+1 dµ′j (lj) K∏ j=k+1 dµj (aj) } for k ∈…”
Reliable Decision Support using Counterfactual Models (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 of a time-dependent outcome.”
TREE-BASED REINFORCEMENT LEARNING FOR ESTIMATING OPTIMAL DYNAMIC TREATMENT REGIMES (2018, background): “…[Murphy, van der Laan and Robins (2001), Wang et al. (2012), Hernán, Brumback and Robins (2001)], G-estimation of structural nested mean models [Robins (1994, 1997, 2004)], targeted maximum likelihood estimators [van der Laan and Rubin (2006)] and likelihood-based approaches [Thall et al.…”