Estimating causal effects from epidemiological data

By Miguel A. Hern'an, James M. Robins. Journal of Epidemiology and Community Health, 2006

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Citations in the corpus

Constructing inverse probability weights for marginal structural models. (2008, background): “Under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate the weights, weighting creates a pseudo-population in which the exposure is independent of the measured confounders (2).”
ipw: An R Package for Inverse Probability Weighting (2011, methodology): “The use of IPW to fit an MSM was described in detail, e.g., in Robins, Hernán, and Brumback (2000), Hernán and Robins (2006) and Cole and Hernán (2008).”
Causal diagrams for interference (2014, background): “We refer the reader to Hernán and Robins (2006) for discussion of these assumptions.”
Observational data for comparative effectiveness research: an emulation of randomised trials to estimate the effect of statins on primary prevention of coronary heart disease (2011, methodology): “Informally, the IP weight for each eligible individual at a given time is the inverse (reciprocal) of her probability of having received the treatment history that she actually received.(22,23) For example, suppose an individual received statins during the first month of follow-up but not during the”
A Review of Domain Adaptation without Target Labels (2019, background): “In clinical studies, randomized controlled trials are used to study the effects of treatments [37].”
The consistency statement in causal inference: a definition or an assumption? (2009, background): “The positivity assumption states that there is a nonzero (ie, positive) probability of receiving every level of exposure for every combination of values of exposure and confounders that occur among individuals in the population.(2,3) It remains unclear why the consistency and positivity assumptions ”
A new criterion for confounder selection (2011, methodology): “As a result, investigators often rely either on a regression model for E(Y |A = a, X = x) or use propensity score techniques (Rosenbaum and Rubin, 1983, 1985; Hernán and Robins, 2006) to estimate causal effects. Pearl, in the context of causal diagrams (Pearl, 1995) gave a graphical criterion to ass”
Estimation of the causal effects of time-varying exposures (2008, background): “When positivity fails to hold for treatment level a, the IPTW formula remains well defined but fails to equal E(Ya), while the g-formula is undefined (Hernán and Robins, 2006a).”
Doubly Robust Estimation of Optimal Dynamic Treatment Regimes (2013, background): “It enables us to estimate causal effects from observational data (see Hernán and Robins [11], for a discussion of the exchangeability assumption).”
Generalizing evidence from randomized clinical trials to target populations: The ACTG 320 trial. (2010, background): “Specifically, trials ensure consistency (1–3) and positivity (1, 4) by design and no unmeasured confounding in expectation by randomization (5, 6).”
Bias-Reduced Doubly Robust Estimation (2015, background): “A causal effect can then be identified if one assumes that there are no unmeasured confounders, the no-unmeasured confounders assumption (Hernán 2004; Hernán and Robins 2006), in the sense that within a specific confounders stratum X , it is as if treatment was randomly assigned.”
Causal inference and counterfactual prediction in machine learning for actionable healthcare (2020, methodology): “In this example, an RCT is conducted in city A (original environment) and a causal effect of treatment x on outcome y , P ( y |do( x )), is determined.”
An introduction to g methods. (2016, methodology): “If we define the residuals (A0− Ê[A0]) as the “instrument,” it follows that Cov{Ỹ ,(A0−Ê[A0])} Cov{A0,(A0−Ê[A0])} yields exactly the estimator in equation (1).”