Causal Inference Colloquium
Is prediction enough?
Stanford colloquium on machine learning and causal inference
Wednesday, April 25, 2018
Please join the Division of Epidemiology and special guests in a multidisciplinary conversation about approaches to deriving causal inferences from algorithmically-driven analyses.
You can view the video recording here.
You can download the agenda here (with video timestamps).
You can download the Speakers' bios here.
Featured Guest: Miguel Hernan, MPH, MD, DrPH
Kolokotrones Professor of Epidemiology and Biostatistics
Harvard T.H. Chan School of Public Health
Miguel Hernan is a Kolokotrones Professor of Biostatistics and Epidemiology and Associate Director of the HSPH program on Causal Inference. His research is focused on methodology for causal inference, including comparative effectiveness of policy and clinical interventions. His work is focused on optimal use of antiretroviral therapy in persons infected with HIV, lifestyle and pharmacological interventions to reduce the incidence of cardiovascular disease, and the effects of erythropoiesis-stimulating agents among dialysis patients. He obtained his medical degree from the Universidad Autonoma de Madid, in Spain, in 1995, and from Harvard University, his MPH in 1996, his ScM in Biostatistics in 1999, and DrPH in Epidemiology in 1999.
Steven Goodman, MD, MHS, PhD is Associate Dean of Clinical and Translational Research and Professor of Medicine and of Health Research & Policy, directing Stanford's CTSA/Spectrum training programs in medical research methods and serving as chief of the Division of Epidemiology in HRP. More His research concerns the proper measurement, conceptualization and synthesis of research evidence, with particular emphasis on Bayesian approaches to quantitation, and qualitative approaches arising from the philosophy of science. Additionally, he has a strong interest in developing curricula and new models for teaching the foundations of good scientific practice, from question development to proper study design, conduct, analysis and inference.
Kristin Sainani (née Cobb) is an associate professor of Health Research and Policy at Stanford University. She teaches statistics and writing; writes about health and science for general audiences; and does research in sports medicine. She is the statistical editor for the journal Physical Medicine & Rehabilitation, and writes a statistics column, Statistically Speaking, for this journal. Her popular Massive Open Online Courses (MOOCs) Writing in the Sciences and Statistics in Medicine have reached large audiences.
Dr. Nigam Shah is associate professor of Medicine (Biomedical Informatics) at Stanford University, Assistant Director of the Center for Biomedical Informatics Research, and a core member of the Biomedical Informatics Graduate Program. His research focuses on combining machine learning and prior knowledge in medical ontologies to enable use cases of the learning health system. More Dr. Shah received the AMIA New Investigator Award for 2013 and the Stanford Biosciences Faculty Teaching Award for outstanding teaching in his graduate class on “Data driven medicine”. Dr. Shah was elected into the American College of Medical Informatics (ACMI) in 2015 and is inducted into the American Society for Clinical Investigation (ASCI) in 2016. He holds an MBBS from Baroda Medical College, India, a PhD from Penn State University and completed postdoctoral training at Stanford University.
Cristobal Young is an Assistant Professor in the Department of Sociology. Cristobal works in the overlapping fields of economic sociology, stratification, and quantitative methods. He studies the sociological dynamics surrounding public policies that moderate income inequality, ranging from millionaire taxes to unemployment insurance. More Methodologically, Cristobal specializes in analyzing large scale administrative and survey data. Drawing on modern computational power, he is developing new methods for addressing model uncertainty and improving the robustness of social science research. He double majored in Economics and Sociology at the University of Victoria, receiving his BA for both in 2001. Afterward, he received his Masters in Economics from the University of Victoria in 2004, another Masters in Sociology from Princeton in 2007, and his PhD in Sociology from Princeton in 2010.
Danton Char is an Assistant Professor in the Department of Anesthesia, Perioperative and Pain Medicine at the Stanford University School of Medicine and a Pediatric Cardiac Anesthesiologist at Lucile Packard Children’s Hospital. His research focuses on ethical issues arising in the care of critically ill neonates, infants and children, particularly children with congenital cardiac disease.
Trevor Hastie is the John A Overdeck Professor of Statistics and of Biomedical Data Science at Stanford University. Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics and machine learning. He has published six books and over 180 research articles in these areas. More Prior to joining Stanford University in 1994, Hastie worked at AT&T Bell Laboratories for 9 years, where he helped develop the statistical modeling environment popular in the R computing system. He received his B.S. in statistics from Rhodes University in 1976, his M.S. from the University of Cape Town in 1979, and his Ph.D from Stanford in 1984.
Ramesh Raskar joined the Media Lab from Mitsubishi Electric Research Laboratories in 2008 as head of the Lab's Camera Culture research group. His research interests span the fields of computational photography, inverse problems in imaging, and human-computer interaction. Recent inventions include transient imaging to look around a corner, a next-generation CAT-scan machine, imperceptible markers for motion capture (Prakash), long-distance barcodes (Bokode), touch + hover 3D interaction displays (BiDi screen), low-cost eye care devices (NETRA) and new theoretical models to augment light fields (ALF) to represent wave phenomena. More In 2004, Raskar received the TR100 Award from Technology Review, presented to top young innovators under the age of 35, and in 2003, the Global Indus Technovator Award, instituted at MIT to recognize the top 20 Indian technology innovators worldwide. In 2009, he was awarded a Sloan Research Fellowship. In 2010, he received the DARPA Young Faculty award. He holds more than 40 US patents, and has received four Mitsubishi Electric Invention Awards. He is currently co-authoring a book on computational photography.
Maria Glymour is a Professor of Epidemiology and Biostatistics at the University of California, San Francisco and co-leads their PhD program in Epidemiology and Translational Science. Her research focuses on healthy aging, and particularly time-varying lifecourse determinants of stroke and dementia (Alzheimer’s, vascular, and mixed etiologies) risk in late life. More To this goal, she draws on both design and data innovations, in particular applying instrumental variables (IV), difference-in-difference, and multilevel models to novel data linkages to evaluate causal hypotheses. Much of her work evaluates social determinants of health and health disparities, topics in which attention to causal inference challenges is critical. She received both her Masters degree and doctorate from Harvard T.H. Chan School of Public Health.
Julia Fridman Simard, ScD, is an Assistant Professor of Health Research and Policy in the Epidemiology Division, and, by courtesy, of Medicine in Immunology and Rheumatology at Stanford University School of Medicine. Dr. Simard’ studies outcomes in systemic autoimmune rheumatic diseases, such as malignancy, stroke, infection, and mortality, but has shifted much of her focus to the intersection between reproductive epidemiology and rheumatic disease. More In 2014 she was awarded a five-year K career development award from the NIH (NIAMS) to study maternal and fetal outcomes in systemic lupus pregnancy. She is also interested in disentangling social and biological constructs in the reported disparities in SLE with respect to sex, gender, race, and ethnicity, both from the etiologic and outcomes perspectives.
Stefan Wager is an Assistant Professor of Operations, Information and Technology at Stanford University’s Graduate School of Business, and an Assistant Professor of Statistics (by courtesy). His research focuses on adapting ideas from machine learning to statistical problems that arise in scientific applications. He is particularly interested in causal inference, non-parametric statistics, uses of subsampling for data analysis, and empirical Bayes methods. More He received his Ph.D. in Statistics from Stanford University in 2016, and also holds a B.Sc. (2011) degree in Mathematics from Stanford. He was a postdoctoral researcher at Columbia University during the academic year 2016-2017, and has worked with or consulted for several Silicon Valley companies, including Dropbox, Facebook and Google.
Mike Baiocchi is an Assistant Professor of Medicine (Stanford Prevention Research Center), and, by courtesy, of Statistics and of Health Research and Policy (Epidemiology). He specializes in creating simple, easy to understand methodologies for causal inference and observational studies. He received his bachelors in Mathematics (with Honors) from Williams College and his PhD in Statistics from the University of Pennsylvania, The Wharton School, in 2011.
Sharad Goel is an assistant professor in the Department of Management Science & Engineering, and holds courtesy appointments in Computer Science and Sociology. He is also the Executive Director of the Stanford Computational Policy Lab. Sharad looks at public policy through the lens of computer science, bringing a computational perspective to a diverse range of contemporary issues, including police practices, bail reform, political polarization, voter fraud, and online privacy. More Before joining Stanford, Sharad was a senior researcher at Microsoft in New York City.
Guido Imbens is Professor of Economics at the Stanford Graduate School of Business. After graduating from Brown University Guido taught at Harvard University, UCLA, and UC Berkeley. He joined the GSB in 2012. Guido does research in econometrics and statistics; his research focuses on developing methods for drawing causal inferences in observational studies, using matching, instrumental variables, and regression discontinuity designs. He is a fellow of the Econometric Society and the American Academy of Arts and Sciences.
Moritz Hardt is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. After obtaining a PhD in Computer Science from Princeton University in 2011, he was a postdoctoral scholar and research staff member at IBM Research Almaden, followed by two years as a research scientist at Google Research and Google Brain. Hardt’s research aims to make the practice of machine learning more robust, reliable, and aligned with societal values.