PPT-Causal Inference & Genetic Regulatory Networks

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. 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 As . empirical data about sets of related entities accrues, there are more constraints on possible network realizations that can fit the data; in the language of statistical mechanics, the size of the microstate ensemble shrinks, until the underlying network resolves. The network inference method . 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 Brian S . Yandell. , . Jee. Young Moon. University of Wisconsin-Madison. Elias . Chaibub. . Neto. , Sage Bionetworks. Xinwei. Deng, . VA Tech. http://www.stat.wisc.edu/~yandell/talk/2012.oslo.pdf. 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. 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. in Development, Evolution and History. Manfred D. Laubichler. Arizona State University. Santa Fe Institute. Marine Biological . Laboratory. Max Planck Institute for the History of Science. John’s Challenge for Future Work:. (. CCD. ). of Biomedical Knowledge from Big . Data. University of Pittsburgh. Carnegie Mellon . University. Pittsburgh Supercomputing . Center. Yale . University. PIs: . Greg Cooper, Ivet . Bahar, Jeremy Berg.