In: Biometrika, 2018, vol. 105, no. 3, p. 575-592
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In: Archives of Orthopaedic and Trauma Surgery, 2015, vol. 135, no. 3, p. 439-445
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In: Journal of Business and Economic Statistics, 2020, vol. 38, no. 1, p. 183-200
This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German...
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In: 2018 IEEE International Conference on Big Data (Big Data), 2018, p. 2253–2262
Large knowledge bases typically contain data adhering to various schemas with incomplete and/or noisy type information. This seriously complicates further integration and post-processing efforts, as type information is crucial in correctly handling the data. In this paper, we introduce a novel statistical type inference method, called StaTIX, to effectively infer instance types in Linked Data...
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In: European Radiology, 2007, vol. 17, no. 4, p. 965-974
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In: Archives of Orthopaedic and Trauma Surgery, 2003, vol. 123, no. 8, p. 425-428
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In: Organisms Diversity & Evolution, 2010, vol. 10, no. 1, p. 69-79
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(Working Papers SES ; 493)
We describe R package “causalweight” for causal inference based on inverse probability weighting (IPW). The “causalweight” package offers a range of semiparametric methods for treatment or impact evaluation and mediation analysis, which incorporates intermediate outcomes for investigating causal mechanisms. Depending on the method, identification relies on selection on observables ...
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In: Bioinformatics, 2014, vol. 30, no. 16, p. 2272-2279
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In: Biometrika, 2014, vol. 101, no. 1, p. 1-15
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