PDF-Implementing Propensity Score Matching Estimators

1 with STATA Barbara SianesiUniversity College LondonInstitute for Fiscal StudiesEmail barbarasifsorgukPrepared forUK Stata Users Group VII MeetingLondon May 2001 ACKGROUNDVALUATION ROBLEM. They enjoy similar consistency and are asymptotically normal although with sometimes higher asymptotic variance There are several reasons for studying these estimators a they may be more comptuationally e64259cient than the MLE b they may be more ro Overview:. What . do we use a propensity score for?. How do we construct the . propensity . score?. How do we implement propensity score estimation in STATA?. Joke (kind of…). Two heart surgeons (Jack and Jill) walk into a bar.. Making Sense of Non-Randomized Observational Data. Atul Sharma MD, MSc, FRCPC(ret). Biostatistical Consulting Unit. April 2014. Propensity score 1996 - 2013. RCT – the gold standard. R.A. Fisher: . Technical Track Session VI. This material constitutes supporting material for the "Impact Evaluation in Practice" book. This additional material is made freely but please acknowledge its use as follows: . Michael . Massoglia. Department of Sociology. University of Wisconsin Madison . General Overview. The logic of propensity models. Application based discussion of some of the key features . Emphasis on working understanding use of models . A Practical Demonstration Looking at Results from the Promise Pathways Initiative at Long Beach City College. Andrew Fuenmayor, Research Analyst. John Hetts, . Director of Institutional Research. Long Beach City College . A Primer in . R. 1. David Zepeda. Assistant Professor. Supply Chain & Information Management. d.zepeda@neu.edu. Center for Health Policy and Healthcare Research. Brown Bag Series. April 1, . 2015. A tutorial with MPLUS. Walter L. Leite, University of Florida. Laura M. Stapleton, University of Maryland. Learning Objectives. Describe quasi-experimental research designs. Identify propensity score analysis methods. Ling . Ning. &. . Mayte. . Frias. . Senior Research Associates. Neil . Huefner. . Associate Director. Timo. Rico. Executive Director. Outline. Understanding causal effects. Methods for estimating causal effects. Austin Nichols (Abt) & Linden McBride (Cornell). July 27, 2017. Stata Conference. Baltimore, MD. Overview. Machine learning methods dominant for classification/prediction problems.. Prediction is useful for causal inference if one is trying to predict propensity scores (probability of treatment conditional on observables);. . Propensity Score Matching as a tool for measuring the effectiveness of Active Labour Market Policies implemented by Croatian Employment Service. Micha. l. Kotnarowski. , . Ph.D. .. . . Institute of Political Studies, Polish Academy of Sciences. Matthew Spotnitz, M.D., M.P.H.. 1. , Karthik Natarajan, Ph.D.. 1. , Patrick B. Ryan, Ph.D.. 2. , Carolyn L. . Westhoff. , M.D., M.Sc.. 1. 1. Columbia University Irving Medical Center, . 2. Janssen Pharmaceuticals. Seng Chan You. What should OHDSI studies look like?. 2. A study should be like a pipeline. A fully automated process from database to paper. ‘Performing a study’ = building the pipeline. Database. Junjing Lin. [Takeda], Margaret Gamalo [Pfizer], . Ram Tiwari. [BMS]. Expanding Real World Evidence in Pre-market Approvals. What Constitutes Externally Controlled Trials? . Potential Outcomes Framework.