Simon Business School

Course Offerings

The Course Catalog (formerly known as the Information Guide) contains degree requirements and course descriptions.

PHD Computer information systems COURSES

CIS 501, 502, 503, 521, 522, 523. PHD SEMINARS IN COMPUTERS AND INFORMATION SYSTEMS

These six PhD seminars are offered in the fall, winter and spring quarters, with topics selected from the following: decision-support systems, economics of information and the valuation of information systems, issues in the management of information systems and the economics of computing, advanced topics in systems analysis and design, organizational aspects of information systems, logical and physical database design and topics discussed in the joint CIS/OMG PhD seminars.

Prerequisite: permission of the instructor

CIS 512. ADVANCED TOPICS IN DATABASE DESIGN

This course examines current research issues in database management systems. Topics include: database-design methodologies, semantic models, semantic integrity constraints, object-oriented approaches and applications of artificial intelligence to database management systems.

Prerequisite: CIS 415 or permission of the instructor

AEC 520. CAUSAL INFERENCE

The course will cover how to design compelling research, the focus of which is causal inference. The course covers the design of true experiments and concepts of validity (internal validity, external validity, replicability). The approach should follow the Rubin potential outcomes framework. The course then covers causal inference and related econometric methods in observational studies for cross-sectional, panel data, and time-series, and non-linear models including OLS, instrumental variables, Heckman selection models, regression discontinuity designs, matched samples designs, granger causality, event studies, diff-in-diff, fixed effects, clustering standard errors, dynamic panel methods (e.g., Blundell and Bond 1998), and some issues in logit/probit/multinomial logit. Although the course will discuss many econometric techniques, students are expected to have already learned the mechanics of these methods, so that the course can focus on causal inference and its limitations in these methodologies.

 

 

 
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