Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches.

By Roderick J. A. Little, Donald B. Rubin. Annual Review of Public Health, 2000

752 citations recorded by the source corpus.

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Citations in the corpus

Behavioral science at the crossroads in public health: extending horizons, envisioning the future. (2006, background): “…have risen and fallen in the history of public health research (see for example Greenland & Brumback, 2002), causal reasoning has generally been based, in whole or in part, on a variant of the counterfactual model (or potential outcome model) (Little & Rubin, 2000; Maldonado & Greenland, 2002).”
Methods for integrating moderation and mediation: a general analytical framework using moderated path analysis. (2007, methodology): “One useful approach to evaluate causality for models such as those in our framework has been developed by Rubin and colleagues (Holland, 1986, 1988; Little & Rubin, 2000; Rubin, 1974, 1978) and has been called the Rubin causal model (RCM).”
The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology. (2004, background): “Rubin (1976) and Little and Rubin (2000) named this the stable unittreatment assumption (SUTVA) and it remains of great interest to epidemiologists (cf. Halloran & Struchiner, 1995; Kaufman & Cooper, 1999).”
Switching to another SSRI or to venlafaxine with or without cognitive behavioral therapy for adolescents with SSRI-resistant depression: the TORDIA randomized controlled trial. (2008, neutral): “Income, median (range), thousands $ 60 (8-500) 62 (0-250) .”
Using an Experimental Evaluation of Charter Schools to Test Whether Nonexperimental Comparison Group Methods Can Replicate Experimental Impact Estimates (2012, background): “…for any differences between the characteristics of the treatment and comparison groups prior to treatment, and that on average, the two groups do not differ on any other unobserved dimensions that are correlated with the outcome(s) of interest (Rosenbaum and Rubin 1983; Little and Rubin 2000).”
Causal Effects In Psychotherapy: Counterfactuals Counteract Overgeneralization (2010, background): “It has been shown to be most useful and relevant in many respects, including etiology, intervention, and the philosophy of science as well as in various fields of health and social sciences (Höfler, 2005; Little & Rubin, 2000; Maldonado & Greenland, 2002; Pearl, 2000; Shadish et al., 2002).”
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery (2021, background): “Although early approaches to statistical inference emphasised that ‘correlation is not causation’ [15], it has since been shown that for certain families of data-generating processes, it is indeed possible to infer causal relationships from purely observational data [37, 29].”
Causal Inference Using Potential Outcomes (2005, background): “For a review of this basic perspective, see the article by Little and Rubin (2000), and for an expanded treatment, including aspects such as traditional econometric instrumental variables analyses, see the forthcoming text by Imbens and Rubin (2005).”
The Bradford Hill considerations on causality: a counterfactual perspective (2005, neutral): “Today, the counterfactual, or potential outcome, model of causality has become more or less standard in epidemiology, and it has been argued that counterfactual causality captures most aspects of causality in health sciences [ 13 ,14].”
Evaluating online ad campaigns in a pipeline: causal models at scale (2010, background): “(See [12], [11] and the references therein, for example.”
A Potential Outcomes View of Value-Added Assessment in Education (2004, methodology): “…we approach value-added assessment from a “potential outcomes” (Rubin Causal Model, RCM) point of view (Rubin 1974, 1978, 2003; Holland 1986; Little and Rubin 2001), with the goal of clarifying the estimation goals and understanding the limitations of data for the types of comparisons…”