Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization

By Keisuke Hirano, Guido W. Imbens. Health Services & Outcomes Research Methodology, 2001

1,189 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9d614c928f31491bd5d6b8c654d09912f03d520b

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

Citations in the corpus

Impact of improved maize varieties on food security in Eastern Zambia: A doubly robust analysis (2018, neutral): “Using simple inverse weights equal to 1 for the treated and p̂ðXÞ ð1 p̂ðXÞÞ for the nontreated, then following Hirano and Imbens (2001), weights can be defined in a combined way as”
Inverse probability weighted estimation for general missing data problems (2004, methodology): “Hirano and Imbens (2002) use linear regression methods, along with propensity score estimation, to estimate the treatment effect of right heart”
CEO Succession Mechanisms, Organizational Context, and Performance: A Socio‐Emotional Wealth Perspective on Family‐Controlled Firms (2014, methodology): “Second, we estimate OLS regressions, weighting observations by the reciprocal of the propensity score (Hirano and Imbens, 2001) within the common support.”
Variable selection for propensity score models. (2006, methodology): “Hirano and Imbens proposed a variable selection strategy for use with a multivariate outcome model employing propensity score weighting [6].”
Race, Neighbourhood Context and Perceptions of Injustice by the Police in Cincinnati (2007, background): “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).”
Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. (2008, background): “Weighting observed values by inverse-probabilities of selection was proposed by Horvitz and Thompson (1952) in the context of survey inference for finite populations.”
A Tutorial on Propensity Score Estimation for Multiple Treatments Using Generalized Boosted Models (2013, methodology): “Authors have used propensity scores to match [1, 2], stratify (subclassify) [3, 4], or weight [5, 6] the samples from the treatment and control groups so that the distributions (or features of the distributions such as the means) of observed pretreatment characteristics are similar across the treatm”
Non-farm work and food security among farm households in Northern Ghana (2011, methodology): “5 Other studies have also employed the logistic model in the estimation of the propensity scores (Hirano and Imbens, 2001; Caliendo and Kopeinig, 2005).”
Does Job Loss Shorten Life? (2009, methodology): “We will adopt a propensity score weighted estimator similar to the estimators proposed in Robins, Hernán, and Brumback (2000), and Hirano and Imbens (2001).”
Use of stabilized inverse propensity scores as weights to directly estimate relative risk and its confidence intervals (2009, methodology): “Propensity scores were introduced by Rosenbaum and Rubin [1, 2] and have been used by many researchers to obtain the treatments effects in observational studies [3-8].”
Strati cation and weighting via the propensity score in estimation of causal treatment e ects : a comparative study (2004, methodology): “Hirano and Imbens [16] report on a practical application of weighted methods and advocate incorporation of regression models as in (9) for this reason.”