Direct and Indirect Causal Effects via Potential Outcomes *

By Donald B. Rubin. 2004

358 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

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Citations in the corpus

Causal Regularization (2019, background): “, [3], addressing different challenges, for instance, high dimensional confounders [4] or the case where some variables other than the common causes are observed [5], just to mention a few of them.”
Causal inference in statistics: An overview (2009, methodology): “22According to Rubin (2004, 2005), R.A. Fisher made this mistake in the context of agriculture experiments.”
Should observational studies be designed to allow lack of balance in covariate distributions across treatment groups? (2009, background): “[11, 12]), as an intensely pragmatic person, that the ‘insights’ offered by the graphical approach to causal inference are generally not helpful, and can too easily lead to theoretical infatuation with mathematical curiosities that lead to bad practical advice.”
Causality, mediation and time: a dynamic viewpoint (2012, background): “Discussion of direct and indirect effects in terms of principal stratification has been given by Rubin (2004).”
Merged municipalities, higher debt: on free-riding and the common pool problem in politics (2010, neutral): “It is important to include only predetermined control variables that are not affected by the treatment since non-predetermined controls affected by the treatment could bias the DiDestimate by absorbing indirect treatment effects working through those controls (Rosenbaum 1994; Rubin 2004).”
The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models (2011, background): “…this interpretation engenders.11 In contrast, the structure-less approach of Rubin (1974) has spawned other definitions of direct effects, normally referred to as “principal-strata direct effect (PSDE)” (Frangakis and Rubin, 2002; Mealli and Rubin, 2003; Rubin, 2004, 2005; Egleston et al., 2010).”
Methods for integrating moderation and mediation: a general analytical framework using moderated path analysis. (2007, methodology): “Applications of the RCM to models that involve mediation (Holland, 1988; Robins & Greenland, 1992; Rubin, 2004) reveal basic difficulties in drawing causal inferences.”
Experimental designs for identifying causal mechanisms (2013, background): “Finally, some advocate the alternative definitions of direct and indirect effects based on principal stratification (Rubin, 2004) and develop new experimental designs to identify them (Mattei and Mealli, 2011).”
Defining and Estimating Intervention Effects for Groups that will Develop an Auxiliary Outcome (2006, background): “…a Bayesian approach, in which prior information is combined with the likelihood to produce a posterior (Imbens and Rubin, 1997), and approaches which derive bounds on causal effects (Balke and Pearl, 1997; Manski, 1990; Robins, 1989; Rubin, 2004; Zhang and Rubin, 2003; Cheng and Small, 2006).”
Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments (2013, background): “This counterfactual nature leads some scholars to question the value of causal mediation analysis (e.g., Rubin 2004).”
Simple relations between principal stratification and direct and indirect effects (2008, background): “In his examples, Rubin (2004) does not give counterfactuals of the form Yaz but only of the form Ya and Za.”
The International Journal of Biostatistics Principal Stratification — a Goal or a Tool ? (2011, positive): “In his words: “The general theme here is that the concepts of direct and indirect causal effects are generally ill-defined and often more deceptive than helpful to clear statistical thinking in real, as opposed to artificial problems” (Rubin, 2004).”
Bounds on Direct Effects in the Presence of Confounded Intermediate Variables (2008, background): “Estimation of the direct effect of a treatment on an outcome is a central concern in epidemiological and clinical research (Robins and Greenland, 1992; Buyse and Molenberghs, 1998; Wang and Taylor, 2002; Rubin, 2004; Kaufman et al., 2005; Taylor, Wang, and Thiebaut, 2005; Petersen, Sinisi, and van d”
Transparent Parametrizations of Models for Potential Outcomes (2012, background): “Rubin (1998, 2004) and Frangakis and Rubin (2002) later coined the term “principal stratum direct e⇤ect”.”
Transportability across studies: A formal approach (2011, background): “Frangakis and Rubin (2002) recognized the causal nature of the problem and have attempted to capture surrogacy without considering graphs or mediation.”
Principal causal effect identification and surrogate end point evaluation by multiple trials (2015, methodology): “…response indicators are used to address non-ignorable non-response problems (Frangakis and Rubin, 1999; Mealli and Pacini, 2008; Mattei et al., 2014), and the potential intermediate variables are used to define direct and indirect effects (Rubin, 2004; Gallop et al., 2009; Mattei and Mealli, 2011).”