AI-Enhanced Learning
Every student learns differently. Our AI tutors are built as customized Google Gemini Gems — each with a distinct pedagogical approach, trained on the course content. "Learn by coding" is also a key feature of these tutors. You can use the Google Colab scratchpad to easily engage with the code the tutors provide.
Recommended for first-time learners
Your step-by-step guide through causal inference. Starts with intuition before introducing formulas, breaks concepts into manageable pieces, and provides worked examples using chapter datasets. Checks your understanding at each stage and guides you to answers with positive reinforcement. Covers all 6 chapters: RCTs, Regression, IV, RD, DD, and the Wages of Schooling.
Try: "Walk me through the RAND Health Insurance Experiment from Chapter 1 step by step"
Best for: Building intuition chapter by chapter, getting unstuck on homework problems
Start Learning →For understanding the stories behind the data
The domain expert who brings each natural experiment to life. Uses a Context-Design-Identification-Policy framework to explain the historical circumstances, institutional details, and policy features that make each study's causal strategy work. Covers 11 case studies including the RAND HIE, Oregon Health Lottery, MDVE, KIPP schools, MLDA mortality, and Great Depression banking.
Try: "Explain the institutional context behind the Oregon Health Plan lottery from Chapter 1"
Best for: Understanding why each causal method works in its real-world setting, preparing policy discussions
Start Learning →For midterm and final preparation
Your test-preparation specialist. Starts with diagnostic questions to find your weak spots, then builds targeted practice sets with increasing difficulty. Drills the distinctions between IV, RD, and DD, provides flashcard-style concept summaries, and flags common exam pitfalls. Question formats include multiple choice, short answer, Wald estimator calculations, and scenario-based problems.
Try: "Quiz me on the difference between IV and RD for Chapters 3 and 4"
Best for: Drilling key concepts before exams, identifying gaps in your understanding of the 5 causal methods
Start Learning →For students with some foundation
Teaches through probing questions, never direct answers. Challenges your assumptions about correlation and causation, pushes you to identify validity threats, and asks the hard questions: ‘Why can’t we just compare?’, ‘What assumption does the heavy lifting?’, ‘Who are the compliers?’ Gives direct help after 3 rounds if you’re stuck, or immediately for Python syntax.
Try: "I think comparing average outcomes between treated and control groups always gives a causal effect"
Best for: Developing critical thinking about identification, preparing to defend research design choices
Start Learning →For hands-on, learn-by-doing coders
Starts with Python code and real data, then builds up to theory. Shows you the result first and explains why it works. Encourages ‘What if’ experiments: change the bandwidth, drop control variables, compare OLS to IV. Uses pandas and pyfixest with all course datasets streamed from GitHub.
Try: "Show me IV estimation of returns to schooling using quarter-of-birth data from Chapter 6"
Best for: Learning causal methods through code, extending chapter notebooks with your own experiments
Start Learning →Pick the learning style that matches your needs and goals.
Ask about any chapter, concept, or dataset. The tutor knows the full curriculum.
Follow the tutor's guidance, run code in Colab, and build your causal inference skills.