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Jason A. Roy, Ph.D.
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
Read more
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
Read more
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!
Register for this course and see more details by visiting:
OpenCourser.com/course/pd592x/a
What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
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.
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.
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.
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Uses the industry standard R Statistical Language, making course transferrable and valuable in the job market
Covers fundamentals of causality identification and analysis, establishing a strong foundation for understanding
Requires no prior knowledge of statistics, making it accessible to students from diverse backgrounds
Modular structure allows students to tailor the learning experience to their specific interests and needs
Taught by Dr. Jason A. Roy, a recognized expert in causal inference, providing students with access to cutting-edge knowledge and insights
Recommended for beginners seeking a foundational understanding of causal effects
Save this course
Save A Crash Course in Causality: Inferring Causal Effects from Observational Data to your list so you can find it easily later:
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Reviews summary
Causal inference with r
Learners say this course offers engaging assignments embedded in well-paced lectures. They appreciate the balance of theory and practice, particularly the use of R programming examples and practice. The course covers a comprehensive set of causal inference techniques, making it a solid foundation for learners. While some mention the instructor's clear explanations, a few express disappointment with the occasional lack of examples and outdated materials. However, the overall sentiment is largely positive.
Covers a wide range of causal inference techniques and concepts.
"This is one of the best online course I have taken so far"
"This course is absolutely worth your time."
Concepts are explained in a clear and comprehensive manner.
"Professor Roy is thoughtful, deliberate and careful in his presentation."
"The instructor explains the concepts very clearly and the slides/examples are instructive."
Introduces concepts and methods through practical exercises.
"Great course. Very clear and practical."
"I really enjoyed this course and I appreciated the practice exercise in R."
Instructor's engagement and communication with learners could be improved.
"I would love to see some more examples from the social sciences."
"The interpretation of analysis results, variations and other subtleties is not the focus of the course."
Some materials, particularly datasets and code, are outdated or have errors.
"Needs to explicity state that R is a requirement to complete course."
"Some datasets are outdated from MatchIt package."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in A Crash Course in Causality: Inferring Causal Effects from Observational Data with these
activities:
Join Study Groups to Discuss Course Topics
Show steps
Enhance understanding through collaborative learning and discussion with peers.
Show steps
-
Form or join study groups with classmates.
-
Meet regularly to discuss course materials, concepts, and assignments.
-
Share ideas, ask questions, and provide support to each other.
Review Book: Econometric Analysis of Cross Section and Panel Data
Show steps
Sharpen skills and knowledge in econometric analysis techniques that directly relate to the course topics.
View
Introductory Econometrics
on Amazon
Show steps
-
Read the assigned chapters thoroughly.
-
Make notes and highlight important concepts.
-
Complete the practice problems at the end of each chapter.
Solve Practice Problems on Directed Acyclic Graphs (DAGs)
Show steps
Strengthen understanding of DAGs and their role in identifying causal relationships.
Show steps
-
Find practice problems online or in textbooks.
-
Attempt to solve the problems independently.
-
Check your solutions against provided answers or online resources.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Implementing Causal Inference Methods in R or Python
Show steps
Gain hands-on experience and improve proficiency in implementing causal inference techniques.
Browse courses on
R
Show steps
-
Find practice datasets and code examples online or in textbooks.
-
Implement the causal inference methods using R or Python.
-
Check your results against provided solutions or compare them with peers.
Take Guided Tutorials on Matching Methods
Show steps
Enhance understanding of matching methods used in causal inference, a core topic in the course.
Show steps
-
Identify tutorials on websites like Coursera, edX, or YouTube.
-
Follow the tutorials step-by-step.
-
Practice implementing the methods in R or other statistical software.
Create a Summary of Causal Inference Methods
Show steps
Reinforce knowledge of different causal inference methods covered in the course.
Browse courses on
Causal Inference
Show steps
-
Gather information from course materials and additional sources.
-
Organize the methods into a clear and concise summary.
-
Use diagrams or examples to illustrate the methods.
-
Share the summary with classmates or post it online for feedback.
Develop a Data Analysis Plan for a Causal Inference Study
Show steps
Apply course concepts to real-world scenarios by developing a plan for a causal inference study.
Browse courses on
Causal Inference
Show steps
-
Define the research question and identify the variables of interest.
-
Choose appropriate causal inference methods based on the research question and data.
-
Develop a detailed plan for data collection, analysis, and interpretation.
-
Present the plan to classmates or an instructor for feedback and refinement.
Attend Workshops on Advanced Causal Inference Techniques
Show steps
Expand knowledge and explore advanced concepts in causal inference through workshops led by experts.
Show steps
-
Research and identify relevant workshops or conferences.
-
Attend the workshops and actively participate in discussions.
-
Network with experts and researchers in the field.
Join Study Groups to Discuss Course Topics
Show steps
Enhance understanding through collaborative learning and discussion with peers.
Show steps
Show steps
Show steps
- Form or join study groups with classmates.
- Meet regularly to discuss course materials, concepts, and assignments.
- Share ideas, ask questions, and provide support to each other.
Review Book: Econometric Analysis of Cross Section and Panel Data
Show steps
Sharpen skills and knowledge in econometric analysis techniques that directly relate to the course topics.
View
Introductory Econometrics
on Amazon
Show steps
Show steps
Show steps
- Read the assigned chapters thoroughly.
- Make notes and highlight important concepts.
- Complete the practice problems at the end of each chapter.
Solve Practice Problems on Directed Acyclic Graphs (DAGs)
Show steps
Strengthen understanding of DAGs and their role in identifying causal relationships.
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Attempt to solve the problems independently.
- Check your solutions against provided answers or online resources.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Implementing Causal Inference Methods in R or Python
Show steps
Gain hands-on experience and improve proficiency in implementing causal inference techniques.
Browse courses on
R
Show steps
Show steps
Show steps
- Find practice datasets and code examples online or in textbooks.
- Implement the causal inference methods using R or Python.
- Check your results against provided solutions or compare them with peers.
Take Guided Tutorials on Matching Methods
Show steps
Enhance understanding of matching methods used in causal inference, a core topic in the course.
Show steps
Show steps
Show steps
- Identify tutorials on websites like Coursera, edX, or YouTube.
- Follow the tutorials step-by-step.
- Practice implementing the methods in R or other statistical software.
Create a Summary of Causal Inference Methods
Show steps
Reinforce knowledge of different causal inference methods covered in the course.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Gather information from course materials and additional sources.
- Organize the methods into a clear and concise summary.
- Use diagrams or examples to illustrate the methods.
- Share the summary with classmates or post it online for feedback.
Develop a Data Analysis Plan for a Causal Inference Study
Show steps
Apply course concepts to real-world scenarios by developing a plan for a causal inference study.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Define the research question and identify the variables of interest.
- Choose appropriate causal inference methods based on the research question and data.
- Develop a detailed plan for data collection, analysis, and interpretation.
- Present the plan to classmates or an instructor for feedback and refinement.
Attend Workshops on Advanced Causal Inference Techniques
Show steps
Expand knowledge and explore advanced concepts in causal inference through workshops led by experts.
Show steps
Show steps
Show steps
- Research and identify relevant workshops or conferences.
- Attend the workshops and actively participate in discussions.
- Network with experts and researchers in the field.
Career center
Learners who complete A Crash Course in Causality: Inferring Causal Effects from Observational Data will develop knowledge and skills
that may be useful to these careers:
Public Health Researcher
Public Health Researcher
Public Health Researchers use statistical methods to investigate the causes of public health problems and develop public health interventions. While most Public Health Researchers focus on observational studies, some specialize in causal inference. This course may be useful to Public Health Researchers who want to advance their careers in causal inference. Specifically, the module on matching and propensity scores and the module on inverse probability of treatment weighting will be of significant value.
Epidemiologist
Epidemiologist
Epidemiologists use statistical methods to investigate the causes of disease and other health problems. While most Epidemiologists focus on observational studies, some specialize in causal inference. This course may be useful to Epidemiologists who want to advance their careers in causal inference. In particular, the module on matching and propensity scores and the module on inverse probability of treatment weighting will be of significant value.
Social Scientist
Social Scientist
Social Scientists use statistical methods to study human behavior and society. While most Social Scientists focus on other topics, some specialize in causal inference. This course may be useful to Social Scientists who want to develop expertise in causal inference. Specifically, the module on matching and propensity scores and the module on inverse probability of treatment weighting will be of significant value.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze clinical trials and other biomedical studies. While most Biostatisticians focus on other topics, some specialize in causal inference. This course may be useful to Biostatisticians who want to develop expertise in causal inference, particularly those who work in areas of clinical research.
Economist
Economist
Economists use statistical methods to analyze economic data and develop economic models. While most Economists focus on other topics, some specialize in causal inference. This course may be useful to Economists who want to develop expertise in causal inference. In particular, the module on instrumental variables methods will be of significant value.
Data Analyst
Data Analyst
Data Analysts use statistical methods to analyze data and extract insights. While most Data Analysts focus on other topics, some specialize in causal inference. This course may be useful to Data Analysts who want to advance their careers in causal inference. In particular, the module on inverse probability of treatment weighting and the module on instrumental variables methods will be of significant value.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical methods to analyze financial data and develop trading strategies. While most Quantitative Analysts focus on other topics, some specialize in causal inference. This course may be useful to Quantitative Analysts who want to develop expertise in causal inference. Particularly, the module on confounding and directed acyclic graphs and the module on instrumental variables methods will be of value.
Data Scientist
Data Scientist
Data Scientists use statistical methods to analyze data and extract insights. While most Data Scientists focus on predictive modeling, some specialize in causal inference. This course may be useful to Data Scientists who want to advance their careers in causal inference. In particular, the module on inverse probability of treatment weighting and the module on instrumental variables methods will be of significant value.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical methods to develop and implement operations research models. While most Operations Research Analysts focus on other topics, some specialize in causal inference. This course may be useful to Operations Research Analysts who want to develop expertise in causal inference, particularly those who work in areas of supply chain management or healthcare operations.
Policy Analyst
Policy Analyst
Policy Analysts use statistical methods to analyze data and develop policies. While most Policy Analysts focus on other topics, some specialize in causal inference. This course may be useful to Policy Analysts who want to develop expertise in causal inference, particularly those who work in areas of social policy.
Business Analyst
Business Analyst
Business Analysts use statistical methods to analyze data and develop business strategies. While most Business Analysts focus on other topics, some specialize in causal inference. This course may be useful to Business Analysts who want to develop expertise in causal inference, particularly those who work in areas of business intelligence.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. While some Statisticians specialize in causal inference, most focus on other topics. Nonetheless, this course may be useful to Statisticians who want to develop expertise in causal inference. Specifically, the module on defining causal effects and the module on confounding and directed acyclic graphs may be particularly helpful.
Market Researcher
Market Researcher
Market Researchers use statistical methods to collect and analyze data about consumer behavior. While most Market Researchers focus on other topics, some specialize in causal inference. This course may be useful to Market Researchers who want to develop expertise in causal inference, particularly those who work in areas of marketing analytics.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While most Software Engineers focus on other areas, some specialize in data science and machine learning. This course may be useful to Software Engineers who are interested in developing expertise in causal inference, particularly those who work in areas of artificial intelligence.
Research Scientist
Research Scientist
Research Scientists use statistical methods to conduct research and develop new knowledge. While some Research Scientists work in the field of causal inference, most focus on other topics. Nonetheless, this course may be of some use to Research Scientists interested in developing expertise in causal inference. Specifically, the module on defining causal effects and the module on confounding and directed acyclic graphs may be particularly helpful.
For more career information including salaries, visit:
OpenCourser.com/course/pd592x/a
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
A Crash Course in Causality: Inferring Causal Effects from Observational Data.
Covers the key concepts of causal inference, from basic definitions to advanced methods. It is written in a clear and concise style, and can serve as a reference for practitioners in diverse disciplines.
Save
Is an excellent reference on causal inference and provides a comprehensive overview of the field. It provides more in-depth coverage of causal graphs and causal assumptions than is possible in a single course. However, the book is quite challenging and may be more suitable for advanced readers.
Save
Provides a comprehensive discussion of counterfactual causality. It covers a wide range of topics, from the foundations of causal inference to the latest advances in causal modeling.
Save
This textbook on econometrics that covers a wide range of topics, including causal inference. It is written in a clear and rigorous style, and is suitable for advanced undergraduate and graduate students.
Provides a comprehensive introduction to causal inference. It covers a wide range of topics, from basic concepts to advanced methods.
Provides an introduction to Bayesian statistics, which powerful approach to causal inference. It is written in a clear and engaging style, and uses R and Stan for data analysis.
Provides a comprehensive introduction to causal inference methods and is written in a relatively accessible style. It includes numerous examples and exercises that are useful for understanding the material.
This textbook covers causal inference methods from an econometrics perspective and includes a variety of applications in economics and other social sciences. It provides a good balance of theoretical and practical content.
Provides a comprehensive overview of structural equation modeling methods and includes applications in educational research and psychology. It good choice for readers who are interested in using structural equation modeling to conduct causal inference analyses.
Provides a comprehensive overview of causal inference methods and is written in a very clear and accessible style. It good choice for readers who are new to the field or who want to learn more about it in a more informal way.
Fun and engaging introduction to causal inference and provides a variety of thought-provoking examples. It good choice for readers who are new to the field or who want to learn more about it in a more informal way.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pd592x/a
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Effort
5 weeks of study, 3-5 hours per week
Level
Intermediate
Via
Coursera
Institution
University of Pennsylvania
Instructor
Jason A. Roy, Ph.D.
Language
English
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Good to know
Uses the industry standard R Statistical Language, making course transferrable and valuable in the job market
Covers fundamentals of causality identification and analysis, establishing a strong foundation for understanding
Requires no prior knowledge of statistics, making it accessible to students from diverse backgrounds
Modular structure allows students to tailor the learning experience to their specific interests and needs
Taught by Dr. Jason A. Roy, a recognized expert in causal inference, providing students with access to cutting-edge knowledge and insights
Recommended for beginners seeking a foundational understanding of causal effects
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