A Crash Course in Causality: Inferring Causal Effects from Observational Data
- Product type
A Crash Course in Causality: Inferring Causal Effects from Observational Data
Provider rating:
Description
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
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 g…
Frequently asked questions
There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
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.
Created by: University of Pennsylvania-
Taught by: Jason A. Roy, Ph.D. , Associate Professor of Biostatistics
Department of Biostatistics, Epidemiology, and Informatics
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from your peersConnect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
CertificatesEarn official recognition for your work, and share your success with friends, colleagues, and employers.
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.Syllabus
WEEK 1
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 videos, 2 practice quizzes expand
- 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
Graded: Causal effects
WEEK 2
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 videos, 1 practice quiz expand
- 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
Graded: Identify from DAGs sufficient sets of confounders
WEEK 3
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 videos, 2 practice quizzes expand
- 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
Graded: Matching
Graded: Propensity score matching
Graded: Data analysis project - analyze data in R using propensity score matching
WEEK 4
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 videos, 1 practice quiz expand
- Video: Intuition for Inverse Probability of Treatment Weighting (IPTW)
- Video: 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
Graded: IPTW
Graded: Data analysis project - carry out an IPTW causal analysis
WEEK 5
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 videos, 2 practice quizzes expand
- 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
Graded: Instrumental variables / Causal effects in randomized trials with non-compliance
Share your review
Do you have experience with this course? Submit your review and help other people make the right choice. As a thank you for your effort we will donate £1.- to Stichting Edukans.There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.