The Role of a Second Control Group in an Observational Study

By Paul R. Rosenbaum. 1987

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mingxiang Li
2. Peter C. Austin
3. G. V. van Breukelen
4. Elizabeth A. Stuart
5. Michael J. Lopez
6. Teemu Kautonen
7. N. Cuong
8. Ruocheng Guo

Citations in the corpus

• Matching With Multiple Control Groups With Adjustment for Group Differences (2008, methodology): “Here we discuss extended caliper matching, which is related to the ideas underlying caliper matching (Althauser & Rubin, 1970; Cochran & Rubin, 1973; Rosenbaum & Rubin, 1983). Stuart (2004) proposed another method that fixes the proportion of matches from one control group, but results in that work ”
• Better Late than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009) (2010, methodology): “On the one hand, economists have now generally adopted Rubin’s potential outcome framework (Rubin 1974, 1990; Rosenbaum and Rubin 1983), labeled the Rubin Causal Model by Paul W. Holland (1986), which formulates causal questions as comparisons of unit-level potential outcomes.”
• Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies (2004, background): “These include the formal role of a second control group (Rosenbaum, 1987) and tests of unconfoundedness (Rosenbaum, 1984).”
• An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, methodology): “…outcomes: propensity score matching, stratification (or subclassification) on the propensity score, inverse probability of treatment weighting (IPTW) using the propensity score, and covariate adjustment using the propensity score (Austin & Mamdani, 2006; Rosenbaum, 1987a; Rosenbaum & Rubin, 1983a).”
• Using the Propensity Score Method to Estimate Causal Effects (2013, methodology): “To overcome the difficulty of estimating the treatment effects using Cochran’s technique, Rosenbaum and Rubin (1983) developed the PSM. The key objective of the PSM is to replace the many confounding covariates in an observational study with one function of these covariates. The function (or the pro”
• Semiparametric Estimation of Average Treatment Eects under Exogeneity: A Review (2003, background): “A number of such tests are developed in Heckman and Hotz (1989) and Rosenbaum (1987).”
• Does Digital Divide or Provide? The Impact of Cell Phones on Grain Markets in Niger (2008, methodology): “Consequently, there is little guidance for evaluating treatment effects in a general equilibrium setting (Rosenbaum 1987).”