About this course: We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). At the end of the course, learners should be able to: 1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!
Who is this class for: Familiarity with traditional statistical methods, such as regression models, and basic probability recommended. Familiarity with free statistical environment R recommended. Learners should successfully download R before starting the course.
Taught by: Jason A. Roy, Ph.D. , Associate Professor of Biostatistics
Department of Biostatistics, Epidemiology, and Informatics
| Level | Intermediate |
| Commitment | 5 weeks of study, 3-5 hours per week |
| Language | English |
| Hardware Req | Learners must download R, the free software environment, in order to complete assessments. |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Welcome to "A Crash Course in Causality"
- Video: Confusion over causality
- Video: Potential outcomes and counterfactuals
- Video: Hypothetical interventions
- Video: Causal effects
- Practice Quiz: Practice Quiz
- Video: Causal assumptions
- Video: Stratification
- Practice Quiz: Practice Quiz
- Video: Incident user and active comparator designs
- Video: Confounding
- Video: Causal graphs
- Video: Relationship between DAGs and probability distributions
- Video: Paths and associations
- Video: Conditional independence (d-separation)
- Practice Quiz: Practice Quiz
- Video: Confounding revisited
- Video: Backdoor path criterion
- Video: Disjunctive cause criterion
- Video: Observational studies
- Video: Overview of matching
- Video: Matching directly on confounders
- Practice Quiz: Practice Quiz
- Video: Greedy (nearest-neighbor) matching
- Video: Optimal matching
- Video: Assessing balance
- Video: Analyzing data after matching
- Practice Quiz: Practice Quiz
- Video: Sensitivity analysis
- Video: Data example in R
- Video: Propensity scores
- Video: Propensity score matching
- Video: Propensity score matching in R
- Video: Intuition for Inverse Probability of Treatment Weighting (IPTW)
- Video: More intuition for IPTW estimation
- Video: Marginal structural models
- Video: IPTW estimation
- Video: Assessing balance
- Practice Quiz: Practice Quiz
- Video: Distribution of weights
- Video: Remedies for large weights
- Video: Doubly robust estimators
- Video: Data example in R
- Video: Introduction to instrumental variables
- Video: Randomized trials with noncompliance
- Video: Compliance classes
- Video: Assumptions
- Practice Quiz: Practice Quiz
- Video: Causal effect identification and estimation
- Video: IVs in observational studies
- Video: Two stage least squares
- Video: Weak instruments
- Practice Quiz: Practice Quiz
- Video: IV analysis in R
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
enjoyed it very much
Not only good for bio stats, it has also profound impact to my understanding of a/b testing in the internet world.
Very approachable as someone with a Masters in Statistics, probably tough if you are not comfortable with notation and concepts of intermediate prob/stats. Extremely clear and concise presentation. Coverage of methodology is a little weak, there is not enough discussion of the dangers of doing causal inference on observational data, nor of the dangers of the proposed methods. For instance, propensity score matching is ineffective or even harmful in the face of hidden confounders, which in the real world you almost always have.
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HS
Overall great! Provides adequate background and practical enough. Can't ask for more.
Thanks