PPT-Estimation of Causal Effects using Propensity Score Weighti

Ling Ning amp Mayte Frias Senior Research Associates Neil Huefner Associate Director Timo Rico Executive Director Outline Understanding causal effects Methods for estimating causal effects. 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 . Susan Athey, Stanford GSB. Based on joint work with Guido Imbens, Stefan Wager. References outside CS literature. Imbens and Rubin Causal Inference book (2015): synthesis of literature prior to big data/ML. 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. Walter Leite. College of Education. University of Florida. Burak. Aydin. Recep. . Tayyip. . Erdo. ğ. an. University. Turkey. Sungur. . Gurel. Siirt. . University. Turkey. Duygu. Cetin-Berber. 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);. Dec 1. , 2016. Learn how to efficiently identify customers most likely to respond to marketing campaigns. 1. PRESENTERS. David Royal, . Client Success Manager. Keaton . Baughan. , . Product Manager. 2. Analytic and Translational Genetics Unit, MGH. Stanley Center for Psychiatric Research & Program in Medical and Population Genetics, Broad Institute. Relatives are more similar than random pairs. Martijn Schuemie. Quick recap of previous meeting. We discussed . some of the proposed best practices. We all agree on the general principles:. Transparency. : others should be able to reproduce your study in every detail using the information you provide.. 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.