The purpose of this course is to examine the recent modifications and extensions to standard methods for hypothesis testing and causal inference. Upon successful completion of this course, students should be able to:
•	Recognize different views of causality in business research and the formulation of research questions.
•	Understand empirical methods for causal inference in observational studies.
•	Describe machine learning concepts and methods.
•	Interpret model estimation results.
•	Acknowledge challenges of drawing causal inferences in empirical research and machine learning.
•	Understand and apply recent modifications and extensions to standard methods for hypothesis testing.
•	Understand and apply machine learning methods to develop independent, dependent, control, and instrument variables.
•	Understand and apply machine learning methods to match samples in quasi-experimental settings.
•	Understand DAG theory and apply DAG graphs to visualize research model.
•	Apply DAG criteria to dragonize models.

-	Different views of causality in business research and the formulation of research questions
-	Empirical methods for causal inference in observational studies
-	Machine learning concepts and methods
-	Model interpretation
-	Challenges of drawing causal inferences in machine learning
-	Recent modifications and extensions to standard methods for hypothesis testing
-	Machine learning for independent, dependent, control, and instrument variables
-	Machine learning for matching samples in quasi-experimental settings
-	Machine learning for probing into the underlying causal mechanisms
-	Machine learning for policy evaluation
-	DAG theory and graphs for research assumptions 
-	Criteria to identify potential biases

As a University of Georgia student, you have agreed to abide by the University's academic honesty policy, "A Culture of Honesty," and the Student Honor Code. All academic work must meet the standards described in "A Culture of Honesty." Lack of knowledge of the academic honesty policy is not a reasonable explanation for a violation. Questions related to course assignments and the academic honesty policy should be directed to the instructor.