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.
制作方： University of Pennsylvania
教学方： Jason A. Roy, Ph.D. , Professor of Biostatistics
Department of Biostatistics and Epidemiology
| 级别 | 中级 |
| 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 |
授课大纲
第 1 周
Welcome and Introduction to Causal Effects
This module focuses on defining causal effects using potential outcomes. A key distinction is made between setting/manipulating values and conditioning on variables. Key causal identifying assumptions are also introduced.
8 个视频, 2 个练习测验
- 视频: Confusion over causality
- 视频: Potential outcomes and counterfactuals
- 视频: Hypothetical interventions
- 视频: Causal effects
- Practice Quiz: Practice Quiz
- 视频: Causal assumptions
- 视频: Stratification
- Practice Quiz: Practice Quiz
- 视频: Incident user and active comparator designs
已评分: Causal effects
第 2 周
Confounding and Directed Acyclic Graphs (DAGs)
This module introduces directed acyclic graphs. By understanding various rules about these graphs, learners can identify whether a set of variables is sufficient to control for confounding.
8 个视频, 1 个练习测验
- 视频: Causal graphs
- 视频: Relationship between DAGs and probability distributions
- 视频: Paths and associations
- 视频: Conditional independence (d-separation)
- Practice Quiz: Practice Quiz
- 视频: Confounding revisited
- 视频: Backdoor path criterion
- 视频: Disjunctive cause criterion
已评分: Identify from DAGs sufficient sets of confounders
第 3 周
Matching and Propensity Scores
An overview of matching methods for estimating causal effects is presented, including matching directly on confounders and matching on the propensity score. The ideas are illustrated with data analysis examples in R.
12 个视频, 2 个练习测验
- 视频: Overview of matching
- 视频: Matching directly on confounders
- Practice Quiz: Practice Quiz
- 视频: Greedy (nearest-neighbor) matching
- 视频: Optimal matching
- 视频: Assessing balance
- 视频: Analyzing data after matching
- Practice Quiz: 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
已评分: Matching
已评分: Propensity score matching
已评分: Data analysis project - analyze data in R using propensity score matching
第 4 周
Inverse Probability of Treatment Weighting (IPTW)
Inverse probability of treatment weighting, as a method to estimate causal effects, is introduced. The ideas are illustrated with an IPTW data analysis in R.
9 个视频, 1 个练习测验
- 视频: More intuition for IPTW estimation
- 视频: Marginal structural models
- 视频: IPTW estimation
- 视频: Assessing balance
- Practice Quiz: Practice Quiz
- 视频: Distribution of weights
- 视频: Remedies for large weights
- 视频: Doubly robust estimators
- 视频: Data example in R
已评分: IPTW
已评分: Data analysis project - carry out an IPTW causal analysis
第 5 周
Instrumental Variables Methods
This module focuses on causal effect estimation using instrumental variables in both randomized trials with non-compliance and in observational studies. The ideas are illustrated with an instrumental variables analysis in R.
9 个视频, 2 个练习测验
- 视频: Randomized trials with noncompliance
- 视频: Compliance classes
- 视频: Assumptions
- Practice Quiz: 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
- Practice Quiz: Practice Quiz
- 视频: IV analysis in R
已评分: Instrumental variables / Causal effects in randomized trials with non-compliance
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制作方
University of Pennsylvania
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
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评分和审阅
Amazing Course! Really Helpful. I would love to have a similar full-duration course :D
gives thorough basic intro to causal inference
This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!
Excellent course. Could use a small restructuring, as I had to go through the material more than once, but otherwise, very good material and presentation.