Design Of Observational Studies

By S. Kruger. 2016

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, methodology): “We also draw on the literature on causal inference, both in experimental and observational settings, Rosenbaum (1995, 2002, 2009), Rubin (2006), Cox (1992), Morgan and Winship (2007), Morton Williams (2010) and Lee (2005), and Imbens and Rubin (2015).”
Learning Representations for Counterfactual Inference (2016, background): “A less desirable consequence is that such models cannot capture difference in the individual treatment ef-t”
Smart, Responsible, and Upper Caste Only: Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles (2019, methodology): “First, we construct control and treatment cohorts of individuals to test for differences in openness using one-to-one almost exact matching (Rosenbaum 2010), which is analogous to techniques like propensity score matching except that it uses direct category alignment.”
Evaluation of subset matching methods and forms of covariate balance (2016, background): “2 of [30] and Chapter 14 of [52] for related discussions).”
Shock-Based Causal Inference in Corporate Finance and Accounting Research (2015, methodology): “For trimming to common support, see Crump et al. (2009); for matching, see Rosenbaum (2010); for inverse propensity weighting, see Busso et al. (2014).”
Machine Learning Methods for Estimating Heterogeneous Causal Eects (2015, methodology): “We consider a setup where either we have data from an experiment where a binary treatment is assigned randomly to units conditional on their observables, or where we have an observational study that satisfies the assumptions for “uncounfoundedness” or “selection on observables,” (Rosenbaum and Rubin”
Impartial Predictive Modeling: Ensuring Fairness in Arbitrary Models (2016, background): “Interested readers are directed toward Pearl (2009); Rosenbaum (2010) for introductions to various perspectives on causal inference.”
Principal Stratification (2015, background): “For further discussion, see Imbens and Rubin (2015) or the first third of Rosenbaum (2010).”
Improving Inter-Laboratory Reproducibility in Measurement of Biochemical Methane Potential (BMP) (2020, background): “Because it was not practical to assign measurement methods to participating laboratories (and no attempt was made to do so), this analysis was effectively observational (not experimental), with a real possibility of confounding [63].”