Causal Inference
Identify cause-and-effect relationships in real-world data, navigate modern techniques, from natural experiments to causal graphs, enabling them to determine whether policies and behaviors actually achieve their intended results.
What This Course Covers
Causal Inference is structured into 11 chapters that build on each other progressively:
Each chapter combines interactive AI tutoring with hands-on examples. After you learn the material, Lambdio's spaced repetition algorithm schedules review sessions at optimal intervals — so you retain concepts and techniques long-term.
How to Study Causal Inference on Lambdio
Lambdio's AI-powered platform adapts to how Economics courses are best learned. Here's our recommended approach:
Causal Inference is a quantitatively rigorous subject that combines formal statistical theory with applied research design. The material spans probability foundations, OLS mechanics, potential outcomes notation, DAG-based causal logic, and the formal assumptions underlying each identification strategy — matching, RDD, IV, fixed effects, DID, and synthetic control. These topics involve mathematical derivations, conditional probability reasoning, and precise logical conditions that benefit from the structured exposition and guided practice that Standard Mode provides. Socratic Mode is less suitable because the dense technical content — the decomposition of the SDO into ATE and selection bias, the mechanics of 2SLS, the formal statement of the Backdoor Criterion — requires direct instruction and worked-through examples rather than open-ended dialogue. The Hard difficulty rating makes High priority the appropriate default, ensuring the spaced repetition algorithm schedules frequent reviews to maintain retention of the interconnected assumptions and methods. For study sessions, use Standard Mode to work through the formal framework of each identification strategy, then Quiz Mode to test recall of key diagnostic concepts such as the conditions for valid instruments, the definition of parallel trends, and the interpretation of LATE. Imagine walking through the identification strategy for a natural experiment — from the theoretical motivation through the empirical specification to the validity tests — with an AI tutor that can push back on your assumptions and help you anticipate reviewer critiques in real time.
Interactive Quiz
Test your knowledge with these sample questions from the course. Click an answer to see if you're right:
What You'll Be Able to Do After This Course
- ✓Explain the distinction between causal inference and correlation-based prediction using the potential outcomes framework
- ✓Apply Bayes Rule and the Law of Total Probability to update causal beliefs conditional on observed evidence
- ✓Diagnose when OLS regression fails to recover causal effects due to violations of the Zero Conditional Mean Assumption
- ✓Formalize causal questions using the Rubin Causal Model and distinguish between ATE, ATT, and LATE estimands
- ✓Construct and interpret Directed Acyclical Graphs to identify confounders, colliders, and mediators in empirical settings
- ✓Implement matching and propensity score methods under the Conditional Independence Assumption
- ✓Apply Regression Discontinuity Design to identify causal effects at cutoff thresholds and validate the design
- ✓Use Instrumental Variables to address endogeneity and interpret the LATE in the presence of heterogeneous treatment effects
- ✓Estimate causal effects using fixed effects models with panel data and diagnose threats to strict exogeneity
- ✓Design Difference-in-Differences and Synthetic Control studies with appropriate placebo and falsification tests
Frequently Asked Questions
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