PPT-1 Big Data, Causal Inference, and Instrumental Variables

Center for Health Policy and Healthcare Research January 22 2015 Steven D Pizer PhD Associate Professor of Health Economics Department of Pharmacy and Health Systems Sciences School of . 1093panmpr013 Causal Inference without Balance Checking Coarsened Exact Matching Stefano M Iacus Department of Economics Business and Statistics University of Milan Via Conservatorio 7 I20124 Mila Technical Track Session IV. 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: . from . Mass Cytometry Data. Presenters: . Ioannis Tsamardinos. and Sofia Triantafillou. Institute of Computer Science, Foundation for Research and Technology, Hellas. Computer Science Department, University of Crete. 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. Center for Health Policy and Healthcare Research. January 22, 2015. Steven D. Pizer, PhD. Associate Professor of Health Economics. Department . of Pharmacy . and Health Systems Sciences . School . of . 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: . Gertler. , P. J.; Martinez, S., . Tony Cox. May 5, 2016. 1. Download free CAT software from: . http://cox-associates.com/CAT.htm. . Outline. Why CAT? Challenges for causal analytics. Ambiguous C-R associations: theory & practice. Alex . Dimakis. UT Austin. j. oint work with . Murat . Kocaoglu. , . Karthik. . Shanmugam. Sriram. . Vishwanath. , . Babak. . Hassibi. Overview. Discovering causal directions . Part 1: . Interventions. Sciences: QUICK EXAMPLES. #. konfoundit. Kenneth A. . Frank. Ran . Xu; Zixi . Chen. ; I-Chien Chen, Guan Saw. 2018. (. AERA on-line video – cost is . $105. ). Motivation . Statistical inferences are often challenged because of uncontrolled bias. There may be bias due to uncontrolled confounding . Sciences: QUICK EXAMPLES. #. konfoundit. Kenneth A. . Frank. Ran . Xu; Zixi . Chen. ; I-Chien Chen, Guan Saw. 2018. (. AERA on-line video – cost is . $105. ). Motivation . Statistical inferences are often challenged because of uncontrolled bias. There may be bias due to uncontrolled confounding . Alex . Dimakis. UT Austin. j. oint work with . Murat . Kocaoglu. , . Karthik. . Shanmugam. Sriram. . Vishwanath. , . Babak. . Hassibi. Overview. Discovering causal directions . Part 1: . Interventions. Comparison of Strategies for Scalable Causal Discovery of Latent Variable Models from Mixed Data Vineet Raghu , Joseph D. Ramsey, Alison Morris, Dimitrios V. Manatakis, Peter Spirtes, Panos K. Chrysanthis, Clark Glymour, and Panayiotis V. Benos Applying Computational Causal Discovery in Biomedicine Greg Cooper, University of Pittsburgh Richard Scheines , Carnegie Mellon University 11/3/2018 Outline Motivation Basics of Causal Graphical Instrumental variables IVs are used to control for confounding and measurement error in ssibility of making causal inferences with observational data Like propensity scores IVconfounding effects Other