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Journal of Evaluation in Clinical Practice|November 7, 2017
Identifying causal mechanisms in health care interventions using classification tree analysisAriel Linden, Paul R YarnoldJournal of Evaluation in Clinical Practice|April 20, 2016
Using machine learning to identify structural breaks in single-group interrupted time series designsAriel Linden, Paul R YarnoldJournal of Evaluation in Clinical Practice|July 17, 2016
Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatmentsAriel Linden, Paul R YarnoldJournal of Evaluation in Clinical Practice|January 26, 2016
Using data mining techniques to characterize participation in observational studiesAriel Linden, Paul R YarnoldJournal of Evaluation in Clinical Practice|March 24, 2016
Using machine learning to assess covariate balance in matching studiesAriel Linden, Paul R YarnoldJournal of Evaluation in Clinical Practice|December 13, 2017
Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weightingAriel Linden, Paul R YarnoldJournal of Evaluation in Clinical Practice|June 12, 2018
Using machine learning to evaluate treatment effects in multiple-group interrupted time series analysisAriel Linden, Paul R YarnoldJournal of Evaluation in Clinical Practice|July 4, 2017
Modeling time-to-event (survival) data using classification tree analysisAriel Linden, Paul R YarnoldJournal of Evaluation in Clinical Practice|July 5, 2017
Minimizing imbalances on patient characteristics between treatment groups in randomized trials using classification tree analysisAriel Linden, Paul R YarnoldJournal of Evaluation in Clinical Practice|June 30, 2016
Combining machine learning and matching techniques to improve causal inference in program evaluationAriel Linden, Paul R YarnoldPageof 14