Causal Inference by David Jensen in Computer Science (June 4 - 5)

Thursday, June 4, 2015 - 9:00am to Friday, June 5, 2015 - 4:00pm

Instructed by David Jensen in Computer Science (June 4 – June 5 | 9:00am - 4:00pm daily)

 

Abstract: 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 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.

Bio: David Jensen is Associate Professor of Computer Science, Director of the Knowledge Discovery Laboratory, and Associated Director of the Computational Social Science Institute at the University of Massachusetts Amherst.  From 1991 to 1995, he served as an analyst with the Office of Technology Assessment, an agency of the United States Congress.  He received his doctorate from Washington University in St. Louis in 1992.  His research focuses on machine learning and causal inference in complex data sets, with applications to social network analysis, computational social science, fraud detection, and management of large technical systems. He has served on the Executive Committee of the ACM Special Interest Group on Knowledge Discovery and Data Mining and on the program committees of the International Conference on Machine Learning, the International Conference on Knowledge Discovery and Data Mining, and the Uncertainty in AI Conference. He won the 2011 Outstanding Teaching Award from the UMass College of Natural Sciences.

 

REGISTER HERE 

Summer Rates (Workshops runs 9 am- 5 pm daily)

Five College

Undergraduate and Graduate Students…………………………..$100 / person

Faculty………………………………………………………………....$200 / person

Non-Five College

Undergraduate and Graduate Students…………………………..$225 / person

Faculty………………………………………………………………....$325 / person