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Causal Inference 2
Michael E. Sobel
Michael E. Sobel
This course offers a rigorous mathematical survey of advanced topics in causal inference at the Master’s level.
Read more
This course offers a rigorous mathematical survey of advanced topics in causal inference at the Master’s level.
Read more
This course offers a rigorous mathematical survey of advanced topics in causal inference at the Master’s level.
Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships.
We will study advanced topics in causal inference, including mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models.
Register for this course and see more details by visiting:
OpenCourser.com/course/3a5fo8/causal
What's inside
Syllabus
Module 7: Introduction to Mediation
Module 8: More on Mediation
Module 9: Instrumental Variables, Principal Stratification, and Regression Discontinuity
Read more
Syllabus
Module 7: Introduction to Mediation
Module 8: More on Mediation
Module 9: Instrumental Variables, Principal Stratification, and Regression Discontinuity
Read more
Module 10: Longitudinal Causal Inference
Module 11: Interference and Fixed Effects
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores diverse advanced topics in causal inference, including mediation, regression discontinuity, and longitudinal causal inference
Suitable for graduate-level students with a strong foundation in statistics
Led by Michael E. Sobel, a recognized expert in causal inference
Examines real-world applications of causal inference methods in various fields, making it relevant to practitioners
Consists of written materials, videos, and interactive exercises, offering a diverse learning experience
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Save Causal Inference 2 to your list so you can find it easily later:
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Reviews summary
Challenging advanced causal inference
Learners say this advanced causal inference course is challenging but rewarding for those with a good foundation in statistics. Students remark that the course is well-structured, with organized lectures and readings. However, they also note that the difficult exams do not always reflect the difficulty of the engaging assignments and that the material is often dry and hard to follow without additional context or examples.
Assignments are engaging.
"This course has whetted my appetite to dig in to the relevant statistics literature in more detail."
Course content is well-structured.
"Excellent treatment of mediation, regression discontinuity, longitudinal causal inference, interference and fixed effects."
Exams do not reflect assignment difficulty.
"Too few and easy assessment questions that does not help understand the course much"
Advanced learners may find this course challenging.
"This course is painful."
"Lots of dry maths with no relatable examples."
"Difficult to follow."
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 Causal Inference 2 with these
activities:
Organize your course resources
Show steps
Stay organized and improve your learning by keeping your course materials well-structured and easily accessible.
Browse courses on
Materials Management
Show steps
-
Review all the resources provided in the course
-
Create folders or a digital system to organize them
-
Establish a systematic approach to filing and accessing materials
-
Regularly review and update your organizational system
Review causality inference concepts
Show steps
Refresh your knowledge of causality inference concepts to strengthen your understanding of the course material.
Browse courses on
Causal Inference
Show steps
-
Read textbooks or online resources to review basic principles
-
Solve practice problems to test your comprehension
-
Prepare notes to summarize key concepts
Explore mediation analysis
Show steps
Enhance your understanding of mediation analysis techniques, which are crucial for understanding causal relationships.
Show steps
-
Find online tutorials or video lectures on mediation analysis
-
Follow along with the steps and examples provided
-
Apply the techniques to practice datasets
-
Summarize your learnings in a written or visual format
Five other activities
Expand to see all activities and additional details
Show all eight activities
Strengthen your longitudinal causal inference skills
Show steps
Reinforce your ability to analyze longitudinal data and draw causal inferences, which is essential in many research areas.
Show steps
-
Work through practice problems or online simulations
-
Analyze real-world datasets using longitudinal causal inference methods
-
Present your findings and discuss the implications
Mentor junior researchers in causal inference
Show steps
Enhance your understanding of causal inference while supporting junior researchers in their learning journey.
Browse courses on
Teaching
Show steps
-
Identify opportunities to mentor junior researchers
-
Provide guidance and support on causal inference concepts and methods
-
Review their work and offer constructive feedback
-
Participate in discussions and share your knowledge
Prepare a simulation study on causal inference methods
Show steps
Develop a deeper understanding of causal inference methods by conducting a simulation study to evaluate their performance.
Browse courses on
Monte Carlo Simulation
Show steps
-
Design the simulation study and define the research questions
-
Implement the simulation using programming language or software
-
Analyze the results and draw conclusions
-
Write a report to document your findings
Contribute to open-source libraries for causal inference
Show steps
Contribute to the community by participating in the development of causal inference tools and libraries.
Browse courses on
Software Development
Show steps
-
Identify open-source projects related to causal inference
-
Understand the project's goals and contribution guidelines
-
Propose and implement improvements or new features
-
Collaborate with other developers and maintain the code
Initiate a project on causal inference applications
Show steps
Apply your knowledge of causal inference to a real-world problem and gain practical experience in implementing causal inference methods.
Show steps
-
Identify a research question or problem that can be addressed using causal inference
-
Develop a research plan and design your study
-
Collect and analyze data using appropriate methods
-
Interpret your findings and draw conclusions
-
Present your work at a conference or publish your findings
Organize your course resources
Show steps
Stay organized and improve your learning by keeping your course materials well-structured and easily accessible.
Browse courses on
Materials Management
Show steps
Show steps
Show steps
- Review all the resources provided in the course
- Create folders or a digital system to organize them
- Establish a systematic approach to filing and accessing materials
- Regularly review and update your organizational system
Review causality inference concepts
Show steps
Refresh your knowledge of causality inference concepts to strengthen your understanding of the course material.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Read textbooks or online resources to review basic principles
- Solve practice problems to test your comprehension
- Prepare notes to summarize key concepts
Explore mediation analysis
Show steps
Enhance your understanding of mediation analysis techniques, which are crucial for understanding causal relationships.
Show steps
Show steps
Show steps
- Find online tutorials or video lectures on mediation analysis
- Follow along with the steps and examples provided
- Apply the techniques to practice datasets
- Summarize your learnings in a written or visual format
Five other activities
Expand to see all activities and additional details
Show all eight activities
Strengthen your longitudinal causal inference skills
Show steps
Reinforce your ability to analyze longitudinal data and draw causal inferences, which is essential in many research areas.
Show steps
Show steps
Show steps
- Work through practice problems or online simulations
- Analyze real-world datasets using longitudinal causal inference methods
- Present your findings and discuss the implications
Mentor junior researchers in causal inference
Show steps
Enhance your understanding of causal inference while supporting junior researchers in their learning journey.
Browse courses on
Teaching
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior researchers
- Provide guidance and support on causal inference concepts and methods
- Review their work and offer constructive feedback
- Participate in discussions and share your knowledge
Prepare a simulation study on causal inference methods
Show steps
Develop a deeper understanding of causal inference methods by conducting a simulation study to evaluate their performance.
Browse courses on
Monte Carlo Simulation
Show steps
Show steps
Show steps
- Design the simulation study and define the research questions
- Implement the simulation using programming language or software
- Analyze the results and draw conclusions
- Write a report to document your findings
Contribute to open-source libraries for causal inference
Show steps
Contribute to the community by participating in the development of causal inference tools and libraries.
Browse courses on
Software Development
Show steps
Show steps
Show steps
- Identify open-source projects related to causal inference
- Understand the project's goals and contribution guidelines
- Propose and implement improvements or new features
- Collaborate with other developers and maintain the code
Initiate a project on causal inference applications
Show steps
Apply your knowledge of causal inference to a real-world problem and gain practical experience in implementing causal inference methods.
Show steps
Show steps
Show steps
- Identify a research question or problem that can be addressed using causal inference
- Develop a research plan and design your study
- Collect and analyze data using appropriate methods
- Interpret your findings and draw conclusions
- Present your work at a conference or publish your findings
Career center
Learners who complete Causal Inference 2 will develop knowledge and skills
that may be useful to these careers:
Causal Inference Researcher
Causal Inference Researcher
Causal Inference Researchers design and conduct studies to determine the effects of interventions or treatments on outcomes. They use statistical methods to analyze data and draw conclusions about the relationships between variables. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Causal Inference Researchers.
Data Scientist
Data Scientist
Data Scientists use statistical methods to analyze data and extract insights from it. They work in a variety of industries, including healthcare, finance, and retail. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Data Scientists.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, interpret, and present data. They work in a variety of industries, including healthcare, finance, and education. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Statisticians.
Health Economist
Health Economist
Health Economists use economic principles to analyze the healthcare system and make recommendations for policy changes. They work in a variety of settings, including government agencies, universities, and healthcare providers. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Health Economists.
Public Health Researcher
Public Health Researcher
Public Health Researchers design and conduct studies to improve the health of populations. They work in a variety of settings, including government agencies, universities, and non-profit organizations. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Public Health Researchers.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Epidemiologists.
Survey Researcher
Survey Researcher
Survey Researchers design and conduct surveys to collect data on public opinion, consumer behavior, and other topics. They use statistical methods to analyze data and make recommendations for policy changes or business decisions. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Survey Researchers.
Market Researcher
Market Researcher
Market Researchers study consumer behavior and preferences. They use statistical methods to analyze data and make recommendations for marketing campaigns. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Market Researchers.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze studies in the biomedical sciences. They work in a variety of settings, including government agencies, universities, and pharmaceutical companies. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Biostatisticians.
Econometrician
Econometrician
Econometricians use statistical methods to analyze economic data. They work in a variety of settings, including government agencies, universities, and financial institutions. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Econometricians.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to solve problems in a variety of industries, including manufacturing, transportation, and healthcare. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Operations Research Analysts.
Risk Analyst
Risk Analyst
Risk Analysts use statistical methods to assess and manage risk in a variety of industries, including finance, insurance, and healthcare. This course provides a strong foundation in the statistical methods used in causal inference, which are essential for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Risk Analysts.
Financial Analyst
Financial Analyst
Financial Analysts use statistical methods to analyze financial data and make recommendations for investment decisions. They work in a variety of settings, including investment banks, hedge funds, and insurance companies. This course provides a strong foundation in the statistical methods used in causal inference, which may be useful for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Financial Analysts.
Data Analyst
Data Analyst
Data Analysts use statistical methods to analyze data and extract insights from it. They work in a variety of industries, including healthcare, finance, and retail. This course provides a strong foundation in the statistical methods used in causal inference, which may be useful for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Data Analysts.
Business Analyst
Business Analyst
Business Analysts use statistical methods to analyze data and make recommendations for business decisions. They work in a variety of industries, including healthcare, finance, and retail. This course provides a strong foundation in the statistical methods used in causal inference, which may be useful for success in this role. The course covers topics such as mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models, which are all relevant to the work of Business Analysts.
For more career information including salaries, visit:
OpenCourser.com/course/3a5fo8/causal
Reading list
We've selected seven 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
Causal Inference 2.
Comprehensive and rigorous introduction to the foundations of causal inference. It covers a wide range of topics including, but not limited to, the identification of causal effects, the estimation of causal effects, and the interpretation of causal effects.
Save
Clear and concise introduction to the basic concepts of causal inference. It is written in a non-technical style and is accessible to readers with a variety of backgrounds.
Comprehensive and technical treatment of the econometrics of causality. It valuable resource for researchers who are interested in using causal inference methods to analyze economic data.
Provides a comprehensive overview of regression discontinuity designs, a powerful quasi-experimental design that can be used to estimate causal effects.
Collection of essays that provide an overview of the current state of the art in causal inference. It valuable resource for researchers who are interested in using causal inference methods to analyze social science data.
Provides a comprehensive overview of longitudinal structural equation modeling, a statistical technique that can be used to analyze longitudinal data.
Save
Provides a popular overview of causal inference, written in a non-technical style that is accessible to a wide range of readers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3a5fo8/causal
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Effort
5 weeks of study, 2-5 hours per week
Level
Advanced
Via
Coursera
Institution
Columbia University
Instructor
Michael E. Sobel
Language
English
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Good to know
Explores diverse advanced topics in causal inference, including mediation, regression discontinuity, and longitudinal causal inference
Suitable for graduate-level students with a strong foundation in statistics
Led by Michael E. Sobel, a recognized expert in causal inference
Examines real-world applications of causal inference methods in various fields, making it relevant to practitioners
Consists of written materials, videos, and interactive exercises, offering a diverse learning experience
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