Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation

By Donald B. Rubin. 2001

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Citations in the corpus

Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, background): “The pitfalls of relying solely on outcome regression or ymodeling have been well documented for causal inference, where the rates of missing information are high and the impact of selection bias can be dramatic (e.g., Rubin, 2001).”
Race, Neighbourhood Context and Perceptions of Injustice by the Police in Cincinnati (2007, background): “Therefore, one would have to invoke “heroic modelling assumptions based on extrapolation” (Rubin, 2001) for this neighbourhood to provide information on the racial differences in perceptions.”
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, methodology): “Rubin (2001) proposed a set of criteria based on comparing the distribution of the propensity score between treated and untreated subjects in a sample to determine whether regression adjustment may inadequately eliminate bias when comparing outcomes between treatment groups.”
Improving propensity score weighting using machine learning (2010, background): “Although some researchers have recommended checking balance on interactions [36, 37], unfortunately it is rarely done in practice.”
Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research (2010, methodology): “Our study uses the standpoint of clearly separating “design” and “analysis” phases for causal inference, as recommended by Rubin and colleagues (Rubin, 2001; Stuart & Rubin, 2007; Rubin, 2008).”
How Generalizable Is Your Experiment? An Index for Comparing Experimental Samples and Populations (2014, methodology): “Interestingly, this rule of thumb corresponds roughly results from Rubin (2001), which suggest that balance is achieved when |d| .25 and ½ ω2 2 (i.e., b > .96).”
On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores (2011, background): “1 SD—and a variance ratio close to one (Rubin, 2001).”
Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology (2006, methodology): “This is imperative for the honesty of the cloning and is in stark contrast to model-based predictive approaches (e.g., see the discussion in Rubin, 2001, 2005; Imbens and Rubin, 2006); this point is emphasized and discussed further in Section 9.”
An introduction to propensity-score methods for reducing confounding in observational studies (2011, positive): “However, when using regression adjustment, the outcome is always in sight, and the researcher is faced with the subtle temptation to continually modify the regression model until the desired association has been achieved (Rubin, 2001).”
Using the Propensity Score Method to Estimate Causal Effects (2013, background): “Rubin (2001) provided additional discussion on covariate adjustment.”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, methodology): “SDM can also be used to evaluate the propensity score, in which case there would only be a single j (Rubin, 2001).”
Crop productivity and adaptation to climate change in Pakistan (2018, background): “The Rubin B and R tests also fall within the bounds suggested by Rubin (2001) to constitute sufficient balance.”