Design of Observational Studies

By Paul R. Rosenbaum. 2009

415 citations recorded by the source corpus.

Beginnings.- Dilemmas and Craftsmanship.- Causal Inference in Randomized Experiments.- Two Simple Models for Observational Studies.- Competing Theories Structure Design.- Opportunities, Devices, and Instruments.- Transparency.- Matching.- A Matched Observational Study.- Basic Tools of Multivariate Matching.- Various Practical Issues in Matching.- Fine Balance.- Matching Without Groups.- Risk-Set Matching.- Matching in R.- Design Sensitivity.- The Power of a Sensitivity Analysis and Its Limit.- Heterogeneity and Causality.- Uncommon but Dramatic Responses to Treatment.- Anticipated Patterns of Response.- Planning Analysis.- After Matching, Before Analysis.- Planning the Analysis.

Subfields: Computer Networks and Communications, Sociology and Political Science, Statistics and Probability

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Citations in the corpus

Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing (2021, methodology): “The specific sensitivity assumption used in this paper is the “marginal sensitivity model” of [56], which is a variant of Rosenbaum’s famous “Γ sensitivity model” [48, 45, 46] that is better suited for IPW analyses.”
A Practical Introduction to Regression Discontinuity Designs (2019, methodology): “The importance of falsification tests and the use of placebo outcomes is generally discussed in the analysis of experiments literature (e.g., Imbens and Rubin, 2015; Rosenbaum, 2002, 2010).”
On Sensitivity Value of Pair-Matched Observational Studies (2017, background): “alue (in a stochastic sense). Previously, this objective is indirectly pursued by maximizing the probability that the p-value upper bound is less than at a xed sensitivity level (Heller et al., 2009, Rosenbaum, 2010a, 2015a). This paper takes the rst step towards directly achieving this goal by esta”
Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations (2017, background): “s a result, they are part of the design stage as opposed to the analysis stage of the study (Rubin, 2008), which helps to maintain the objectivity of the study and preserve the validity of its tests (Rosenbaum, 2010). Furthermore, weighting methods are considered to be multipurpose in the sense that”
Regression Discontinuity Designs (2021, positive): “Schochet (2009) discusses power calculations and optimal sample size selection for experimental design using parametric regression specifications for RD treatment effect estimation.”
Long-Term Economic Distress, Cultural Backlash, and Support for Brexit (2019, background): “The first one is the existence of posttreatment bias (Rosenbaum, 1984, 2010), which refers to adjusting for characteristics that can be affected by exposure to economic decline (e.g., the level of development, migration flows, sociodemographic characteristics, etc.).”