Fetching the paper…
Reading the bibliography…
In observational studies, propensity scores are commonly estimated by maxi- mum likelihood but may fail to balance high-dimensional pre-treatment covariates even after specification search.
Matching in analytical studies
Cochran, W. G. (1953) · 1953
Earlier work this paper cites.
The effectiveness of adjustment by subclassification in removing bias in observational studies
Cochran, W. G. (1968) · 1968
Earlier work this paper cites.
Elicitation of personal probabilities and expectations
Savage, L. J. (1971) · 1971
Earlier work this paper cites.
Matching to remove bias in observational studies
Rubin, D. B. (1973) · 1973
Earlier work this paper cites.
The central role of the propensity score in observational studies for causal effects
Rosenbaum, P. and D. Rubin (1983) · 1983
Earlier work this paper cites.
Classification and regression trees
Breiman, L., J. Friedman, C. J. Stone, and R. A. Olshen (1984) · 1984
Earlier work this paper cites.
Reducing bias in observational studies using subclassification on the propensity score
Rosenbaum, P. and D. Rubin (1984) · 1984
Earlier work this paper cites.
Constructing a control group using multivariate matched sampling methods that incorporate the propensity score
Rosenbaum, P. R. and D. B. Rubin (1985) · 1985
Earlier work this paper cites.
Generalized linear models
McCullagh, P. and J. A. Nelder (1989) · 1989
Earlier work this paper cites.
Spline models for observational data
Wahba, G. (1990) · 1990
Earlier work this paper cites.
Calibration estimators in survey sampling
Deville, J.-C. and C.-E. Särndal (1992) · 1992
Earlier work this paper cites.
Estimation of regression coefficients when wome regressors are not always observed
Robins, J. M., A. Rotnitzky, and L. Zhao (1994) · 1994
Earlier work this paper cites.
Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme
Heckman, J. J., H. Ichimura, and P. E. Todd (1997) · 1997
Earlier work this paper cites.
Integral probability metrics and their generating classes of functions
Müller, A. (1997) · 1997
Earlier work this paper cites.
Additive logistic regression: a statistical view of boosting
Friedman, J., T. Hastie, and R. Tibshirani (1998) · 2000
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Friedman, J. H. (2001) · 2001
Earlier work this paper cites.
Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization
Hirano, K. and G. Imbens (2001) · 2001
Earlier work this paper cites.
Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores
Normand, S.-L. T., M. B. Landrum, E. Guadagnoli, J. Z. Ayanian, T. J. Ryan, P. D. Cleary, and B. J. McNeil (2001) · 2001
Cited alongside, same era.
Efficient estimation of average treatment effects using the estimated propensity score
Hirano, K., G. W. Imbens, and G. Ridder (2003) · 2003
Cited alongside, same era.
Least angle regression
Efron, B., T. Hastie, I. Johnstone, and R. Tibshirani (2004) · 2004
Cited alongside, same era.
Nonparametric estimation of average treatment effects under exogeneity: A review
Imbens, G. W. (2004) · 2004
Cited alongside, same era.
Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Lunceford, J. K. and M. Davidian (2004) · 2004
Cited alongside, same era.
Matching methods for causal inference: A review and a look forward
Stuart, E. A. (2010) · 2010
Later among the works it cites.
Entropy balancing for causal effects: a multivariate reweighting method to produce balanced samples in observational studies
Hainmueller, J. (2011) · 2011
Later among the works it cites.
Inverse probability tilting for moment condition models with missing data
Graham, B. S., C. C. D. X. Pinto, and D. Egel (2012) · 2012
Later among the works it cites.
A kernel two-sample test
Gretton, A., K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola (2012) · 2012
Later among the works it cites.
Covariate balancing propensity score
Imai, K. and M. Ratkovic (2014) · 2014
Later among the works it cites.
Moving towards best practice when using inverse probability of treatment weighting (iptw) using the propensity score to estimate causal treatment effects in observational studies
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Loss functions for binary class probability estimation and classification: Structure and applications
Buja, A., W. Stuetzle, and Y. Shen (2005) · 2005
Cited alongside, same era.
Does matching overcome lalonde’s critique of nonexperimental estimators?
Smith, J. A. and P. E. Todd (2005) · 2005
Cited alongside, same era.
Large sample properties of matching estimators for average treatment effects
Abadie, A. and G. W. Imbens (2006) · 2006
Cited alongside, same era.
Moving the goalposts: Addressing limited overlap in the estimation of average treatment effects by changing the estimand
Crump, R., V. J. Hotz, G. Imbens, and O. Mitnik (2006) · 2006
Cited alongside, same era.
Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and A. E. Raftery (2007) · 2007
Cited alongside, same era.
Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
Kang, J. D. and J. L. Schafer (2007) · 2007
Cited alongside, same era.
Some practical guidance for the implementation of propensity score matching
Caliendo, M. and S. Kopeinig (2008) · 2008
Cited alongside, same era.
Austin, P. C. and E. A. Stuart (2015) · 2015
Later among the works it cites.
Causal inference for statistics, social, and biomedical sciences
Imbens, G. W. and D. B. Rubin (2015) · 2015
Later among the works it cites.
Stable weights that balance covariates for estimation with incomplete outcome data
Zubizarreta, J. R. (2015) · 2015
Later among the works it cites.
Approximate residual balancing: De-biased inference of average treatment effects in high dimensions
Athey, S., G. W. Imbens, S. Wager, et al. (2016) · 2016
Closest in time.
Globally efficient nonparametric inference of average treatment effects by empirical balancing calibration weighting
Chan, K. C. G., S. C. P. Yam, and Z. Zhang (2016) · 2016
Closest in time.
Improving covariate balancing propensity score: A doubly robust and efficient approach
Fan, J., K. Imai, H. Liu, Y. Ning, and X. Yang (2016) · 2016
Closest in time.
Kernel balancing: A flexible non-parametric weighting procedure for estimating causal effects
Hazlett, C. (2016) · 2016
Closest in time.
Eigenprism: inference for high dimensional signal-to-noise ratios
Janson, L., R. F. Barber, and E. Candès (2016) · 2016
Closest in time.
Generalized optimal matching methods for causal inference
Kallus, N. (2016) · 2016
Closest in time.
Balancing covariates via propensity score weighting
Li, F., K. L. Morgan, and A. M. Zaslavsky (2016) · 2016
Closest in time.
Entropy balancing is doubly robust
Zhao, Q. and D. Percival (2016) · 2016
Closest in time.