A Crash Course in Causality: Inferring Causal Effects from Observational Data
提供方
A Crash Course in Causality: Inferring Causal Effects from Observational Data
University of Pennsylvania
课程信息
4.8
92 个评分
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!
您将获得的技能
Instrumental VariablePropensity Score MatchingCausal InferenceCausality
教学大纲 - 您将从这门课程中学到什么
1
章节
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（共 128 分钟）, 3 个测验
8 个视频
Confusion over causality19分钟
Potential outcomes and counterfactuals13分钟
Hypothetical interventions17分钟
Causal effects19分钟
Causal assumptions18分钟
Stratification23分钟
Incident user and active comparator designs14分钟
3 个练习
Practice Quiz4分钟
Practice Quiz4分钟
Causal effects18分钟
2
章节
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（共 86 分钟）, 2 个测验
8 个视频
Confounding6分钟
Causal graphs9分钟
Relationship between DAGs and probability distributions15分钟
Paths and associations7分钟
Conditional independence (d-separation)13分钟
Confounding revisited9分钟
Backdoor path criterion15分钟
Disjunctive cause criterion9分钟
2 个练习
Practice Quiz8分钟
Identify from DAGs sufficient sets of confounders22分钟
3
章节
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（共 171 分钟）, 5 个测验
12 个视频
Overview of matching12分钟
Matching directly on confounders13分钟
Greedy (nearest-neighbor) matching17分钟
Optimal matching10分钟
Assessing balance11分钟
Analyzing data after matching20分钟
Sensitivity analysis10分钟
Data example in R16分钟
Propensity scores11分钟
Propensity score matching14分钟
Propensity score matching in R15分钟
5 个练习
Practice Quiz6分钟
Practice Quiz8分钟
Matching12分钟
Propensity score matching10分钟
Data analysis project - analyze data in R using propensity score matching16分钟
4
章节
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（共 119 分钟）, 3 个测验
9 个视频
More intuition for IPTW estimation9分钟
Marginal structural models11分钟
IPTW estimation11分钟
Assessing balance9分钟
Distribution of weights9分钟
Remedies for large weights13分钟
Doubly robust estimators15分钟
Data example in R26分钟
3 个练习
Practice Quiz6分钟
IPTW18分钟
Data analysis project - carry out an IPTW causal analysis8分钟
讲师
Jason A. Roy, Ph.D.
Professor of BiostatisticsDepartment of Biostatistics and Epidemiology
关于 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.
常见问题
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
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