JSM 2015 Preliminary Program

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Legend: Washington State Convention Center = CC, Sheraton Seattle = S, Grand Hyatt = GH and The Conference Center = TCC
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Activity Details

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CE_16C Mon, 8/10/2015, 8:30 AM - 5:00 PM S-Willow A
Guidelines for Using State-of-the-Art Methods to Estimate Propensity Score and Inverse Probability of Treatment Weights When Drawing Causal Inferences (ADDED FEE) — Professional Development Continuing Education Course
ASA
Estimation of causal effects is a primary activity of many studies. Examples include testing whether a substance abuse treatment program is effective, whether an intervention improves the quality of mental health care, or whether incentives improve retention of military service members. Controlled experiments are the gold standard for estimating such effects. However, experiments are often infeasible, forcing analysts to rely on observational data in which treatment assignments are out of the control of the researchers. This short course will provide an introduction to causal modeling using the potential outcomes framework and the use of propensity scores and weighting (i.e., propensity score or inverse probability of treatment weights) to estimate causal effects from observational data. It also will present step-by-step guidelines for estimating and performing diagnostic checks of the estimated weights for testing the relative effectiveness of two or more interventions and the cumulative effects of time-varying interventions. Attendees will gain hands-on experience estimating propensity score weights using boosted models and covariate balancing propensity scores in R, SAS and Stata; evaluating the quality of those weights; and using them to estimate intervention effects. Attendees should be familiar with linear and logistic regression; no knowledge of propensity scores is expected.
Instructor(s): Lane Burgette, RAND Corporation, Daniel F. McCaffrey, Educational Testing Service




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