BEST PRACTICES IN QUASI- EXPERIMENTAL DESIGNS Matching Methods for Causal Inference

By Elizabeth A. Stuart, Donald B. Rubin. 2007

415 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/212ddc753963dd564686dbc422d1c5d498838a3f

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Improving propensity score weighting using machine learning (2010, background): “Covariate balance : One rule of thumb for assessing the covariate balance between treatment groups is that an absolute standardized difference in means of 0.2 or greater may be of concern [15, 33].”
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).”
Un sistema de clasificación de los diseños de investigación en psicología (2013, background): “Para compensar esta ausencia se recurre al empleo de grupos de tratamiento, grupos de control, medidas pretest y postest y técnicas de control experimental (p.e., métodos de emparejamiento, véase Stuart & Rubin, 2008) con el objeto de controlar las diferencias preexistentes entre grupos.”
Is the SEC captured? Evidence from comment-letter reviews (2017, methodology): “…AUC in Column 1, Panel B (i.e., using PAC as measure of PC) is 0.883, suggesting the models have reasonable ability to correctly predict firm PC. 9 Stuart and Rubin (2007) note that “… a drawback of matching with replacement is that it may be that only a few unique control units will be selected…”
The effects of public support schemes on small and medium enterprises (2015, methodology): “The most popular among these methods are the propensity score and Mahalanobis distance methods (Stuart and Rubin, 2007).”
Estimation of causal effects with multiple treatments: a review and new ideas (2017, methodology): “(27) For all of the matching algorithms and at each p, δp1, the standard deviation of Xp in the full sample among those receiving reference t1, is used for standardization, to ensure that observed differences in the similarity of those matched are easily contrasted (as in Stuart and Rubin (2008)).”