Standard syllabus
Causal inference · Graduate · Math
Learning objectives from the Causal inference syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Graduate Causal inference — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Potential outcomes framework
- Treatment effects: ATE, ATT, and CATE — Treatment effects: ATE, ATT, and CATE
- Randomized experiments as gold standard — Randomized experiments as gold standard
- SUTVA and consistency assumptions — SUTVA and consistency assumptions
- Bias decomposition: confounding — Bias decomposition: confounding
- Selection — Selection
- DAGs for causal identification (introduction) — DAGs for causal identification (introduction)
Quasi-experimental methods
- Matching and propensity scores — Matching and propensity scores
- Inverse probability weighting — Inverse probability weighting
- Difference-in-differences — Difference-in-differences
- Regression discontinuity designs — Regression discontinuity designs
- Instrumental variables for causal effects — Instrumental variables for causal effects
Advanced identification
- Mediation analysis (introduction) — Mediation analysis (introduction)
- Sensitivity analysis for unmeasured confounding — Sensitivity analysis for unmeasured confounding
- Synthetic control methods (overview) — Synthetic control methods (overview)
- Causal inference with time-varying treatments (intro) — Causal inference with time-varying treatments (intro)
Learning objectives
Click an objective for study materials.
Potential outcomes framework
- Treatment effects: ATE, ATT, and CATE — Treatment effects: ATE, ATT, and CATE
- Randomized experiments as gold standard — Randomized experiments as gold standard
- SUTVA and consistency assumptions — SUTVA and consistency assumptions
- Bias decomposition: confounding — Bias decomposition: confounding
- Selection — Selection
- DAGs for causal identification (introduction) — DAGs for causal identification (introduction)
Quasi-experimental methods
- Matching and propensity scores — Matching and propensity scores
- Inverse probability weighting — Inverse probability weighting
- Difference-in-differences — Difference-in-differences
- Regression discontinuity designs — Regression discontinuity designs
- Instrumental variables for causal effects — Instrumental variables for causal effects
Advanced identification
- Mediation analysis (introduction) — Mediation analysis (introduction)
- Sensitivity analysis for unmeasured confounding — Sensitivity analysis for unmeasured confounding
- Synthetic control methods (overview) — Synthetic control methods (overview)
- Causal inference with time-varying treatments (intro) — Causal inference with time-varying treatments (intro)
Definitions and structure
- Treatment effects: ATE, ATT, and CATE — Treatment effects: ATE, ATT, and CATE
- Randomized experiments as gold standard — Randomized experiments as gold standard
- SUTVA and consistency assumptions — SUTVA and consistency assumptions
- Bias decomposition: confounding — Bias decomposition: confounding
- Selection — Selection
Proofs and reasoning
- DAGs for causal identification (introduction) — DAGs for causal identification (introduction)
- Matching and propensity scores — Matching and propensity scores
- Inverse probability weighting — Inverse probability weighting
- Difference-in-differences — Difference-in-differences
- Regression discontinuity designs — Regression discontinuity designs
Abstraction and generalization
- Instrumental variables for causal effects — Instrumental variables for causal effects
- Mediation analysis (introduction) — Mediation analysis (introduction)
- Sensitivity analysis for unmeasured confounding — Sensitivity analysis for unmeasured confounding
- Synthetic control methods (overview) — Synthetic control methods (overview)
- Causal inference with time-varying treatments (intro) — Causal inference with time-varying treatments (intro)
Modeling and computation
- Apply treatment effects: ate, att, and cate in engineering contexts — Apply treatment effects: ate, att, and cate in engineering contexts
- Apply randomized experiments as gold standard in engineering contexts — Apply randomized experiments as gold standard in engineering contexts
- Apply sutva and consistency assumptions in engineering contexts — Apply sutva and consistency assumptions in engineering contexts
- Apply bias decomposition: confounding in engineering contexts — Apply bias decomposition: confounding in engineering contexts
- Apply selection in engineering contexts — Apply selection in engineering contexts
Data and technology
- Use software to explore causal inference problems numerically — Use software to explore causal inference problems numerically
- Interpret computational results against analytic predictions — Interpret computational results against analytic predictions
- Build spreadsheets or scripts for routine calculations — Build spreadsheets or scripts for routine calculations
- Visualize functions, fields, or datasets tied to course topics — Visualize functions, fields, or datasets tied to course topics
- Connect course methods to lab, industry, or research workflows — Connect course methods to lab, industry, or research workflows
Problem-solving practice
- DAGs for causal identification (introduction) — DAGs for causal identification (introduction)
- Matching and propensity scores — Matching and propensity scores
- Inverse probability weighting — Inverse probability weighting
- Difference-in-differences — Difference-in-differences
- Regression discontinuity designs — Regression discontinuity designs
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Potential outcomes framework
Treatment effects: ATE, ATT, and CATE
Coming soon - Quasi-experimental methods
Matching and propensity scores
Coming soon - Advanced identification
Mediation analysis (introduction)
Coming soon
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$1,162 · Causal inference · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.