关于此课程： 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!
此课程适用人群： 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.
教学方： Jason A. Roy, Ph.D. , Associate Professor of Biostatistics
Department of Biostatistics, Epidemiology, and Informatics
| 级别 | Intermediate |
| 承诺学习时间 | 5 weeks of study, 3-5 hours per week |
| 语言 | English |
| 硬件要求 | Learners must download R, the free software environment, in order to complete assessments. |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- 视频: Welcome to "A Crash Course in Causality"
- 视频: Confusion over causality
- 视频: Potential outcomes and counterfactuals
- 视频: Hypothetical interventions
- 视频: Causal effects
- Cuestionario de práctica: Practice Quiz
- 视频: Causal assumptions
- 视频: Stratification
- Cuestionario de práctica: Practice Quiz
- 视频: Incident user and active comparator designs
- 视频: Confounding
- 视频: Causal graphs
- 视频: Relationship between DAGs and probability distributions
- 视频: Paths and associations
- 视频: Conditional independence (d-separation)
- Cuestionario de práctica: Practice Quiz
- 视频: Confounding revisited
- 视频: Backdoor path criterion
- 视频: Disjunctive cause criterion
- 视频: Observational studies
- 视频: Overview of matching
- 视频: Matching directly on confounders
- Cuestionario de práctica: Practice Quiz
- 视频: Greedy (nearest-neighbor) matching
- 视频: Optimal matching
- 视频: Assessing balance
- 视频: Analyzing data after matching
- Cuestionario de práctica: Practice Quiz
- 视频: Sensitivity analysis
- 视频: Data example in R
- 视频: Propensity scores
- 视频: Propensity score matching
- 视频: Propensity score matching in R
- 视频: Intuition for Inverse Probability of Treatment Weighting (IPTW)
- 视频: More intuition for IPTW estimation
- 视频: Marginal structural models
- 视频: IPTW estimation
- 视频: Assessing balance
- Cuestionario de práctica: Practice Quiz
- 视频: Distribution of weights
- 视频: Remedies for large weights
- 视频: Doubly robust estimators
- 视频: Data example in R
- 视频: Introduction to instrumental variables
- 视频: Randomized trials with noncompliance
- 视频: Compliance classes
- 视频: Assumptions
- Cuestionario de práctica: Practice Quiz
- 视频: Causal effect identification and estimation
- 视频: IVs in observational studies
- 视频: Two stage least squares
- 视频: Weak instruments
- Cuestionario de práctica: Practice Quiz
- 视频: IV analysis in R
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
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| 访问课程材料 | 可用 |
| 访问评分的材料 | 可用 |
| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
Excellent course! The lectures are very clear and easy to follow, and Professor Roy is really good at explaining the concepts in a simple way. The assignments in R are helpful for grasping the theoretical concepts. I would specially recommend this course to data scientist, who might be interested in complementing their predictive analytics skills with the the necessary ones to tackle questions about causality.
I learned the basics of causality inference and want even more now!
Great course for getting your hands dirty with some real causal methods.
One of the best courses in Coursera, Professor with lots of experience in a backpack show how to tackle very complex problem of causal inference. This is a topic every data analyst should know doesn't matter which industry you work or learn.