Informações sobre o curso: 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!
Para quem é direcionado este curso: 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.
Ministrado por: Jason A. Roy, Ph.D. , Associate Professor of Biostatistics
Department of Biostatistics, Epidemiology, and Informatics
| Nível | Intermediate |
| Compromisso | 5 weeks of study, 3-5 hours per week |
| Idioma | English |
| Requisitos de hardware | Learners must download R, the free software environment, in order to complete assessments. |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
- Vídeo: Confusion over causality
- Vídeo: Potential outcomes and counterfactuals
- Vídeo: Hypothetical interventions
- Vídeo: Causal effects
- 练习测验: Practice Quiz
- Vídeo: Causal assumptions
- Vídeo: Stratification
- 练习测验: Practice Quiz
- Vídeo: Incident user and active comparator designs
- Vídeo: Causal graphs
- Vídeo: Relationship between DAGs and probability distributions
- Vídeo: Paths and associations
- Vídeo: Conditional independence (d-separation)
- 练习测验: Practice Quiz
- Vídeo: Confounding revisited
- Vídeo: Backdoor path criterion
- Vídeo: Disjunctive cause criterion
- Vídeo: Overview of matching
- Vídeo: Matching directly on confounders
- 练习测验: Practice Quiz
- Vídeo: Greedy (nearest-neighbor) matching
- Vídeo: Optimal matching
- Vídeo: Assessing balance
- Vídeo: Analyzing data after matching
- 练习测验: Practice Quiz
- Vídeo: Sensitivity analysis
- Vídeo: Data example in R
- Vídeo: Propensity scores
- Vídeo: Propensity score matching
- Vídeo: Propensity score matching in R
- Vídeo: More intuition for IPTW estimation
- Vídeo: Marginal structural models
- Vídeo: IPTW estimation
- Vídeo: Assessing balance
- 练习测验: Practice Quiz
- Vídeo: Distribution of weights
- Vídeo: Remedies for large weights
- Vídeo: Doubly robust estimators
- Vídeo: Data example in R
- Vídeo: Randomized trials with noncompliance
- Vídeo: Compliance classes
- Vídeo: Assumptions
- 练习测验: Practice Quiz
- Vídeo: Causal effect identification and estimation
- Vídeo: IVs in observational studies
- Vídeo: Two stage least squares
- Vídeo: Weak instruments
- 练习测验: Practice Quiz
- Vídeo: IV analysis in R
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
I have an economics background and during my undergraduate studies I took several statistics and econometric courses. The contents delivered in this course complemented my knowledge very well from another point of view. I would definitely enjoy a more advanced course dealing with other methods. The only aspect I would improve is providing the slides for further study. Other courses in Coursera do this and, honestly, I often consult the slides.
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.
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.
A
This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!