20903 - CAUSAL INFERENCE FOR BUSINESS POLICIES

In our interactive lectures, you'll gain insights into marketing analytics and acquire the skills to write your own computer code. In the initial portion of the course, we'll focus on mastering the basics of regression analysis, decision trees, and the R language. As we progress into the latter part of the course, we'll delve into advanced methods for data analysis and prediction. An integral aspect of the course involves students presenting the outcomes of a group project they've conceptualized and analyzed.

Topics that can be expected during the course:
Regression analysis
Regression and decision trees
Model assessment
Random forest and Lasso
Matching
Difference-in-differences
Endogeneity
Causal forests
Models for individual level and aggregate level data
Selected further topics: e.g., online reviews, product search, recommendation systems
To provide a real-world perspective, guest speakers are invited to elaborate on the data skills and approaches sought after by companies. This exposure aims to enhance your understanding of the applications of the skills acquired throughout the course.

• Grasp the pivotal role of data analysis in the field of business, and appreciate its impact on decision-making processes.
• Develop a keen intuition for selecting and applying appropriate data analysis methods based on the specific demands and nuances of business scenarios.
• Independently write computer code for analysis, thereby fostering a practical approach to leverage data in business contexts.

• Assess and determine the most suitable type of analysis required for a given business setting, aligning analytical approaches with the specific needs and objectives of the company.
• Present the findings of their own data analysis such that both experts and non-experts can learn from it.
• Formulate recommendations to managers regarding the types of data that need to be collected to address a given managerial question.
• Demonstrate an understanding of the critical data sources necessary for informed decision-making.
• Develop actionable recommendations on profitable marketing campaigns tailored to specific contexts.

• Face-to-face lectures
• Guest speaker's talks (in class or in distance)
• Exercises (exercises, database, software etc.)
• Group assignments

Guest speakers

Guest speakers will share their experience on working in companies as managers or analysts, and will give a good understanding to students regarding the expectations from employees.

Exercises

We will cover a variety of coding exercises in R, either in class or as home assignment.

Group assignments

Students will work in groups on their group project.

Group Assignment:

Students will engage in group assignments, where their collaborative efforts will be assessed. The quality of group work, including the application of analytical methods, data interpretation, and presentation skills, will contribute to the overall evaluation.

Final Presentations Bonus Points:

Recognition will be given to student groups delivering the best final presentations, as voted by their peers.

Written Exam:

The final written exam will be based on the topics covered during class. It will evaluate students’ understanding and ability to apply the methods, models, and tools we have discussed. The exam does not include any elements related to computer coding, nor does it contain questions pertaining to coding.

Not attending students will be evaluated based on a final written exam. The exam will cover specific chapters selected from the required textbook. (The exam does not include any elements related to computer coding, nor does it contain questions pertaining to coding.)

Békés, Gábor, and Gábor Kézdi. Data Analysis for Business, Economics, and Policy. Cambridge University Press, 2021.

RStudio (free version) installed on laptop.

Békés, Gábor, and Gábor Kézdi. Data Analysis for Business, Economics, and Policy. Cambridge University Press, 2021.