***THIS WORKSHOP WILL NOTE BE HELD June 6-7. IT WILL BE RESCHEDULED FOR LATER THIS SUMMER. Please contact Jessica Pearlman at email@example.com if you have any questions***
This workshop will be offered in person on the UMass campus.
Description: Inferring causality is central to many quantitative studies in social science. A large number of analytical methods have been developed to infer causal dependence from observational data, including propensity score matching, instrumental variable designs, interrupted time-series designs, and many others. Unfortunately, the assumptions and limitations of these methods can be difficult to explain and reason about.
This 2-day (12-hour) tutorial introduces participants to causal graphical models, a powerful formalism developed within computer science and statistics that simultaneously provides: 1) a unifying formal framework for understanding and explaining specific methods for causal inference; 2) a practical tool for representing and reasoning about the implications of particular causal models; and 3) powerful algorithmic methods for learning complex causal models from data and reasoning about their implications. This tutorial assumes only a basic understanding of probability and statistics and no knowledge of programming. Participants familiar with experimental and quasi-experimental designs will gain a new understanding of the benefits and assumptions of these methods, and participants without that knowledge will learn about multiple methods for inferring causality within a single unifying framework.
Instructor: David Jensen
David Jensen is a Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He serves as the Director of the Computational Social Science Institute, an interdisciplinary effort at UMass to study social phenomena using computational tools and concepts. His current research focuses on machine learning and data science for analyzing large social, technological, and computational systems. In particular, his work focuses on methods for constructing accurate causal models from observational and experimental data, with applications to social science, fraud detection, security and systems management. In 2011, he received the Outstanding Teacher Award from the UMass College of Natural Sciences.
Questions? For more information about this or any of the ISSR Summer Methodology Workshops, please contact ISSR Director of Research Methods Programs Jessica Pearlman (firstname.lastname@example.org).
REGISTRATION INFORMATION | 12-HOUR WORKSHOP
Important: If you are registering for more than one workshop, please submit a separate registration form for each workshop, and select the appropriate fee for the length of your workshop and your academic and institutional status. We apologize for the inconvenience, but if the incorrect fee is selected your registration will be have to be resubmitted. Your original registration will be canceled and your fee returned to you.
Five College Students and Faculty
- Five College Undergraduate and Graduate Students: $150/person
- Five College Faculty and Staff: $250/person
Non-Five College Students and Faculty
- Non-Five College Undergraduate and Graduate Students: $275/person
- Non-Five College Faculty, Staff and Other Professional: $400/person
Registration note: The Five Colleges include: UMass Amherst, Amherst College, Hampshire College, Mount Holyoke College, and Smith College. Registration closes for each workshop 2 full business days prior to the start date. If paying with departmental funds or personal checks, contact Sue Falcetti (email@example.com).
Cancellation note: In cases where enrollment is 5 or less, we reserve the right to cancel the workshop. In cases where the registrant cancels prior to the workshop, a full refund will be given with two weeks notice, and 50% refund will be given with one week notice. We will not be able to refund in cases where registrant does not notify us of cancellation at least one week prior to the beginning date of the workshop.