Instructor: Franz Buscha
Is y really equal to 0.5x? Is education really good for you? Is taxation policy really changing spending behavior? To answer such questions, you often need to infer causality from survey data. To do that, you need to understand the empirical tools available to data analysts.

In this course, professor of economics Franz Buscha explains the fundamentals of causal inference; strategies for overcoming common pitfalls in survey data analysis; and concepts around experimental, quasi-experimental, and non-experimental estimators. Franz delves into the methodologies for drawing causal inference from survey data. He accomplishes this over three chapters focusing on: experimental and randomized control trials, cross-sectional survey data and how to draw out causal relationships, and longitudinal surveys and methods for causal inference. Plus, Franz presents a brief overview of the methods to evaluate the robustness of empirical findings and techniques to communicate them effectively.

Learning objectives

• Understand the principles and importance of causal inference
  
• Learn to interpret survey data with a causal inference lens
  
• Gain skills in handling common data challenges in causal survey data analysis
  
• Acquire knowledge of advanced causal inference techniques and their applications
  
• Learn how to communicate such findings effectively
  

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