PPT-Causal Inference for Complex

Observational Data Using Stata Chuck Huber StataCorp chuberstatacom ERMs Outline Description of the dataset Unobserved confounding and endogeneity Nonrandom treatment assignment Missing not at random MNAR and selection bias. 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 Inference on Causal Effects in aGeneralized Regression Kink DesignIZA DP No. 8757David CardDavid S. LeeZhuan PeiAndrea Weber Inference on Causal Effects in aGeneralized Regression Kink DesignDavid Car Yusu. . Wang. Ohio State University. AMS Short Course 2014. Introduction. Much recent developments in computational topology. Both in theory and in their applications. E.g. , the theory of persistence homology. 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. Peter Spirtes. Carnegie Mellon University. With slides from Lizzie Silver. Outline. Biology. Data and Background Knowledge. Problems. Algorithms. Causal Graph. gene protein. mRNA mRNA . protein gene. Session 6. Course : T0273 – EXPERT SYSTEMS. Year : 2014. Learning Outcomes. LO 2 : Describe the characteristics of Expert Systems. After taking this course, students should be expected to explain and use the Method of inference.. Kenneth A. Frank . Guan Saw, UT San Antonio. AERA workshop April 4, 2014 (. AERA on-line video – cost is $95. ). Motivation . Statistical inferences are often challenged because of uncontrolled bias. There may be bias due to uncontrolled confounding . 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);. 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. Exploring Epidemiologic and Econometric Approaches to Causal Inference SERdigital , March 9, 2017 Miguel Hernán departments of epidemiology and biostatistics Causal inference from observational data Anne Morse [. Huércanos. ], PhD. Estimates and Projections Area. Population Division. This presentation is released to inform interested parties of ongoing research and to encourage discussion of work in progress. Any views expressed are those of the authors and not necessarily those of the U.S. Census Bureau..