Propensity score estimation with boosted regression for evaluating causal effects in observational studies.

By Daniel F McCaffrey, Greg Ridgeway, A. Morral. 2004

1,318 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

How researchers see this paper

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

In Statistics and Probability

In Causal Inference & Discovery

Citations in the corpus

Approximate residual balancing: debiased inference of average treatment effects in high dimensions (2016, background): “(1976) and Robins et al. (1994) obtained their weights γi for equation (4) via propensity score modelling instead of quadratic programming.”
Propensity score estimation: machine learning and classification methods as alternatives to logistic regression (2010, methodology): “have used generalized boosted models (a decision treeebased boosting technique that provides probabilities) to successfully generate propensity scores [8,41].”
Boosting Algorithms: Regularization, Prediction and Model Fitting. Rejoinder. (2007, methodology): “Boosting methods for estimating propensity scores, a special weighting scheme for modeling observational data, are proposed in [63].”
Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments (2008, methodology): “Other methods for creating propensity scores exist, including classification trees, boosted regression, random forests, and boosted regression (e.g., Stone et al. 1995; McCaffrey, Ridgeway, and Morral 2004).”
Bias-Reduced Doubly Robust Estimation (2015, methodology): “Next we consider the DR estimator μ̂GBM which uses a PS estimated under a generalized boosted model (GBM), a multivariate nonparametric regression technique (McCaffrey, Ridgeway, and Morral 2004).”
The Blessings of Multiple Causes (2018, background): “Schneeweiss et al. (2009), McCaffrey, Ridgeway, and Morral (2004), Lee, Lessler, and Stuart (2010), and many others develop and evaluate different models for assigned causes.”
Race, Neighbourhood Context and Perceptions of Injustice by the Police in Cincinnati (2007, methodology): “Propensity score weighting deals better with confounding variables that are correlated with both race and the outcome of interest (perceptions of injustice by the police) (Rosenbaum, 1987; Hirano and Imbens, 2001; Wooldridge, 2002; Imbens, 2004; McCaffrey et al., 2004, Ridgeway, 2006).”
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, methodology): “Although logistic regression appears to be the most commonly used method for estimating the propensity score, the use of bagging or boosting ( Lee, Lessler, & Stuart, 2010 ; McCaffrey, Ridgeway, & Morral, 2004 ), recursive partitioning or tree-based methods ( Lee et al, 2010 ; Setoguchi, Schneewei”
Propensity Score Analysis: Recent Debate and Discussion (2020, methodology): “More recently, newmodels—such as the application of generalized boosted modeling (McCaffrey et al., 2004)—have been developed to refine logistic regression and, in turn, to refine propensity score estimation. The greatest advantage of the propensity score is its reduction in dimensions, which solves”
Doubly Robust Internal Benchmarking and False Discovery Rates for Detecting Racial Bias in Police Stops (2009, methodology): “For more detailed analysis of propensity score weights see Hirano and Imbens (2001), Wooldridge (2001, pp. 614–621), McCaffrey, Ridgeway, and Morral (2004), and Ridgeway (2006).”
Do the short-term effects of a high-quality preschool program persist? (2015, methodology): “Boosted regression can use all available covariates and is not subject to the particular modeling choices made by the analyst (Guo & Fraser, 2010; McCaffrey et al., 2004; Stuart, 2010).”