Courses
Causal Inference Education Pathways
Our curriculum is designed to take you from foundational concepts to cutting-edge methods in causal inference. Each course builds on the previous one, but you can also jump in at the level that matches your background.
Empirical Research Fundamentals
BeginnerBasic knowledge and skills about empirical research in social sciences, with a particular focus on quantitative research and relevant mathematical and/or statistical foundations
Introduction to Causal Inference
Beginner/IntermediateIntroductory course to causal inference, including topics like potential outcomes model, directed acyclic graphs, basic research designs, and randomized control trials as the benchmark approach
Intermediate Causal Inference
IntermediateTypical, well-established, non-RCT methods for causal inference, such as subclassification, matching methods, difference-in-differences, panel fixed effects, instrumental variables, etc.
Advanced Causal Inference
AdvancedMore advanced topics and recent developments in causal inference, including machine learning, meta-learners, tree-based approaches, etc.
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Course Details
1. Empirical Research Fundamentals
Beginner level • Prerequisites: Basic statistics
This course establishes the foundation for all empirical research in social sciences, with particular focus on quantitative methods. **Learning Objectives:** - Understand the research process from question formulation to interpretation - Master basic statistical concepts: distributions, hypothesis testing, confidence intervals - Learn data management and visualization principles - Develop critical thinking about research design and measurement **Topics Covered:** - Research question development - Measurement theory and operationalization - Descriptive statistics and data visualization - Probability distributions and sampling - Hypothesis testing and p-values - Confidence intervals and effect sizes - Introduction to regression analysis - Research ethics and reproducibility **Format:** - 8 modules with video lectures - Weekly problem sets with solutions - Final project: Design a research study - Recommended: 6-8 hours per week for 8 weeks **Resources:** - Textbook: *The Practice of Social Research* by Earl Babbie - Software: R or Python (your choice) - Datasets: Real-world social science data2. Introduction to Causal Inference
Beginner/Intermediate level • Prerequisites: Empirical Research Fundamentals or equivalent
This course introduces the core framework for thinking causally, starting with the potential outcomes model and building up to randomized experiments as the gold standard. **Learning Objectives:** - Understand Rubin's potential outcomes framework - Define and identify causal effects - Design and analyze randomized controlled trials (RCTs) - Critically evaluate causal claims in research **Topics Covered:** - Potential outcomes and the fundamental problem of causal inference - Stable Unit Treatment Value Assumption (SUTVA) - Average Treatment Effects (ATE, ATT, ATC) - Randomized experiments: design, analysis, and interpretation - Directed Acyclic Graphs (DAGs) for causal reasoning - Confounding, selection bias, and measurement error - Instrumental variables introduction - Regression discontinuity design introduction **Format:** - 10 modules with interactive content - Simulation exercises to build intuition - Case studies of landmark RCTs - Final exam with applied problems - Recommended: 8-10 hours per week for 10 weeks **Resources:** - Textbook: *Causal Inference: What If* by Hernán and Robins (free online) - Software: R with `causalverse` package - Datasets: Experimental data from economics, medicine, education3. Intermediate Causal Inference
Intermediate level • Prerequisites: Introduction to Causal Inference
This course covers established methods for estimating causal effects when randomization is not possible, focusing on observational study designs. **Learning Objectives:** - Apply matching and weighting methods to balance covariates - Implement difference-in-differences designs - Use panel data methods for causal inference - Understand instrumental variables estimation - Conduct sensitivity analyses for unobserved confounding **Topics Covered:** - Propensity score methods: matching, stratification, weighting - Inverse probability weighting and doubly robust estimation - Difference-in-differences: two-way fixed effects, event studies - Panel data methods: fixed effects, random effects, first differences - Instrumental variables: two-stage least squares, local average treatment effects - Regression discontinuity: sharp and fuzzy designs - Synthetic control method - Sensitivity analysis for unobserved confounding **Format:** - 12 modules with technical depth - Weekly coding assignments with peer review - Replication of published studies - Final project: Analyze an observational dataset - Recommended: 10-12 hours per week for 12 weeks **Resources:** - Textbook: *Mastering 'Metrics* by Angrist and Pischke - Software: R with `MatchIt`, `fixest`, `ivpack` packages - Datasets: Observational data from economics, public health, policy evaluation4. Advanced Causal Inference
Advanced level • Prerequisites: Intermediate Causal Inference
This course explores frontier topics and modern approaches, including machine learning methods for causal inference and recent methodological developments. **Learning Objectives:** - Integrate machine learning with causal inference - Estimate heterogeneous treatment effects - Handle high-dimensional confounding - Understand recent advances in causal discovery - Apply causal inference to complex data structures **Topics Covered:** - Machine learning for causal inference: meta-learners, causal forests - Heterogeneous treatment effects: estimation and inference - High-dimensional confounding and double/debiased machine learning - Causal discovery from observational data - Mediation analysis and path-specific effects - Time-varying treatments and dynamic regimes - Network interference and spillover effects - Bayesian methods for causal inference - Recent advances: synthetic interventions, proximal causal inference **Format:** - 14 modules at research level - Reading and discussion of recent papers - Implementation of advanced methods - Research proposal development - Recommended: 12-15 hours per week for 14 weeks **Resources:** - Textbook: *Causal Inference for Statistics, Social, and Biomedical Sciences* by Imbens and Rubin - Software: R with `grf`, `DoubleML`, `bnlearn` packages - Research papers from top econometrics, statistics, and ML journalsEnrollment and Access
Current Status
All courses are under development and will be released in phases:
1. **Phase 1 (Q3 2024)**: Introduction to Causal Inference 2. **Phase 2 (Q4 2024)**: Empirical Research Fundamentals 3. **Phase 3 (Q1 2025)**: Intermediate Causal Inference 4. **Phase 4 (Q2 2025)**: Advanced Causal Inference ### Format Options Each course will be available in multiple formats: - **Self-paced online**: Video lectures, interactive exercises, automated feedback - **Live cohort**: Weekly sessions with instructor, peer discussion, personalized feedback - **Institutional licensing**: For universities and research organizations ### Prerequisites Assessment Not sure which course is right for you? Take our [prerequisites assessment quiz](#) (coming soon) to determine the best starting point. ### Scholarships We are committed to making causal inference education accessible. Limited scholarships will be available for: - Students from low-income countries - Researchers at institutions with limited resources - Professionals transitioning to data science/research roles ### Certification Complete all four courses to earn a **Certificate in Causal Inference Methods**. Each course also offers individual completion certificates.Frequently Asked Questions
Q: Do I need to take the courses in order?
A: While designed as a sequence, each course stands alone. We recommend starting with "Introduction to Causal Inference" unless you have strong statistics background.
Q: What software will I need?
A: All courses use R, with optional Python equivalents provided. We'll help you get set up regardless of your current skill level.
Q: Are there prerequisites?
A: Yes, each course lists prerequisites. The first course requires only basic statistics knowledge.
Q: How much time should I budget?
A: See the "Format" section for each course. Most require 6-15 hours per week depending on the level.
Q: Will there be interaction with instructors?
A: In live cohort formats, yes. Self-paced courses include discussion forums and optional office hours.
Q: Can I use these courses for academic credit?
A: We're working on partnerships with universities for credit transfer. Currently, courses provide certificates of completion.
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