Corpus ID: 233872691

Double score matching estimators of average and quantile treatment effects

@inproceedings{Yang2020DoubleSM,
  title={Double score matching estimators of average and quantile treatment effects},
  author={Shu Yang and Yunshu Zhang},
  year={2020}
}
Propensity score matching has a long tradition for handling confounding in causal inference. In this article, we propose double score matching estimators of the average treatment effects and the quantile treatment effects utilizing two balancing scores including the propensity score and the prognostic score. We show that the de-biasing double score matching estimators achieve the double robustness property in that they are consistent for the true causal estimands if either the propensity score… Expand

Figures and Tables from this paper

Double Robust Mass-Imputation with Matching Estimators
This paper proposes using a method named Double Score Matching (DSM) to do mass-imputation and presents an application to make inferences with a nonprobability sample. DSM is a k-Nearest NeighborsExpand

References

SHOWING 1-10 OF 83 REFERENCES
Matching on the Estimated Propensity Score
Propensity score matching estimators (Rosenbaum and Rubin (1983)) are widely used in evaluation research to estimate average treatment effects. In this article, we derive the large sampleExpand
Estimating average treatment effects with a double-index propensity score.
TLDR
A novel PS estimator, the Double-index Propensity Score (DiPS), is proposed, in which the treatment status is smoothed over the linear predictors for X from both the initial working models, which leads to gains in efficiency and robustness over traditional doubly-robust estimators. Expand
Doubly robust matching estimators for high dimensional confounding adjustment.
TLDR
This article derives asymptotic results for the matching estimator and shows that it is doubly robust in the sense that only one of the two score models need be correct to obtain a consistent estimator. Expand
Bootstrap Inference of Matching Estimators for Average Treatment Effects
ABSTRACT It is known that the naive bootstrap is not asymptotically valid for a matching estimator of the average treatment effect with a fixed number of matches. In this article, we proposeExpand
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
TLDR
It is shown that weighting with the inverse of a nonparametric estimate of the propensity Score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects, whether the pre-treatment variables have discrete or continuous distributions. Expand
Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence
  • Zhong Zhao
  • Computer Science, Economics
  • Review of Economics and Statistics
  • 2004
TLDR
Through a series of simulations, the small-sample properties of propensity-score matching versus covariate matching estimators, and of different matching metrics, through Monte Carlo experiments, are studied. Expand
Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data
TLDR
This work proposes alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods, even with some estimated propensity scores close to zero. Expand
The central role of the propensity score in observational studies for causal effects
Abstract : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group.Expand
Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores
&NA; Causal inference with observational studies often relies on the assumptions of unconfoundedness and overlap of covariate distributions in different treatment groups. The overlap assumption isExpand
Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.
Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score,Expand
...
1
2
3
4
5
...