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Anders Holm
How can we know if the differences in wages between men and women are caused by discrimination or differences in background characteristics? In this PhD-level course we look at causal effects as opposed to spurious relationships. We will discuss how they can...
Read more
How can we know if the differences in wages between men and women are caused by discrimination or differences in background characteristics? In this PhD-level course we look at causal effects as opposed to spurious relationships. We will discuss how they can...
Read more
How can we know if the differences in wages between men and women are caused by discrimination or differences in background characteristics? In this PhD-level course we look at causal effects as opposed to spurious relationships. We will discuss how they can be identified in the social sciences using quantitative data, and describe how this can help us understand social mechanisms.
Register for this course and see more details by visiting:
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possible dealbreakers
Develops causal relationships using quantitative data and social sciences
Taught by Anders Holm, a recognized expert in causal inference
Uses a PhD-level approach to causal analysis, making it suitable for advanced students
Examines real-world examples of wage discrimination, offering practical insights
Builds a strong foundation in causal inference for those interested in research or policy analysis
Requires familiarity with quantitative methods and a solid understanding of statistics
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Reviews summary
Meaningful social sciences research
This PhD-level course in causal effects is a meaningful experience to researchers in social sciences. It comes with essential terminologies. However, there is limited interaction between students and faculty.
Introduces important terms.
"I found this course to be particularly useful, as it introduces lots of common terminologies used for social science researches."
Limited interaction.
"This is an very useful course for me who is studying Health Economics. However, the lack of interaction between lecturer and student makes this course more difficult. "
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 Measuring Causal Effects in the Social Sciences with these
activities:
Brush up on algebra
Show steps
A solid grasp of basic algebra is necessary for understanding the concepts of causal inference.
Browse courses on
Algebra
Show steps
-
Review your algebra notes from high school or college.
-
Take an online algebra refresher course or watch tutorials.
-
Practice solving algebra problems in a workbook or online.
Review required course texts
Show steps
Reviewing your course texts beforehand will improve your comprehension and retention of concepts as they are introduced in class.
View
Studyguide for Mostly Harmless Econometrics
on Amazon
Show steps
-
Read the preface and introduction of each text.
-
Read the first chapter of each book.
Complete the Coursera tutorial on causal inference
Show steps
Understanding causal inference is key to this course. This tutorial will solidify your conceptual understanding and improve your ability to follow the lectures.
Show steps
-
Go to the Coursera website and find the tutorial.
-
Watch the videos and read the accompanying materials.
-
Complete the quizzes at the end of each section.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group
Show steps
Studying with peers can help you to understand the material better and stay motivated.
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss the material and work on problems together.
-
Help each other to understand difficult concepts.
Solve practice problems on causal inference
Show steps
Practice is essential for developing your skills in causal inference. Regularly solving problems will improve your problem-solving skills and understanding of the concepts.
Browse courses on
Causal Inference
Show steps
-
Find practice problems in your textbook or online.
-
Solve the problems and check your answers against the solutions.
-
Join a study group to discuss the problems and learn from others.
Find a mentor who can provide guidance on causal inference
Show steps
Having a mentor can help you to learn more about causal inference and develop your skills.
Show steps
-
Identify someone who has experience in causal inference.
-
Ask them if they would be willing to mentor you.
-
Meet with your mentor regularly to discuss your progress and get feedback.
Write a blog post explaining a causal inference concept
Show steps
Teaching a concept is one of the best ways to learn it. By writing a blog post, you will deepen your understanding of causal inference and improve your communication skills.
Show steps
-
Choose a causal inference concept that you are familiar with.
-
Write a blog post explaining the concept in a clear and concise way.
-
Share your blog post with others and ask for feedback.
Participate in a causal inference competition
Show steps
Participating in a competition can help you to test your skills and learn from others.
Show steps
-
Find a causal inference competition that you are interested in.
-
Form a team or work on your own.
-
Develop a solution to the problem.
-
Submit your solution and compete for prizes.
Brush up on algebra
Show steps
A solid grasp of basic algebra is necessary for understanding the concepts of causal inference.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Review your algebra notes from high school or college.
- Take an online algebra refresher course or watch tutorials.
- Practice solving algebra problems in a workbook or online.
Review required course texts
Show steps
Reviewing your course texts beforehand will improve your comprehension and retention of concepts as they are introduced in class.
View
Studyguide for Mostly Harmless Econometrics
on Amazon
Show steps
Show steps
Show steps
- Read the preface and introduction of each text.
- Read the first chapter of each book.
Complete the Coursera tutorial on causal inference
Show steps
Understanding causal inference is key to this course. This tutorial will solidify your conceptual understanding and improve your ability to follow the lectures.
Show steps
Show steps
Show steps
- Go to the Coursera website and find the tutorial.
- Watch the videos and read the accompanying materials.
- Complete the quizzes at the end of each section.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group
Show steps
Studying with peers can help you to understand the material better and stay motivated.
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss the material and work on problems together.
- Help each other to understand difficult concepts.
Solve practice problems on causal inference
Show steps
Practice is essential for developing your skills in causal inference. Regularly solving problems will improve your problem-solving skills and understanding of the concepts.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Find practice problems in your textbook or online.
- Solve the problems and check your answers against the solutions.
- Join a study group to discuss the problems and learn from others.
Find a mentor who can provide guidance on causal inference
Show steps
Having a mentor can help you to learn more about causal inference and develop your skills.
Show steps
Show steps
Show steps
- Identify someone who has experience in causal inference.
- Ask them if they would be willing to mentor you.
- Meet with your mentor regularly to discuss your progress and get feedback.
Write a blog post explaining a causal inference concept
Show steps
Teaching a concept is one of the best ways to learn it. By writing a blog post, you will deepen your understanding of causal inference and improve your communication skills.
Show steps
Show steps
Show steps
- Choose a causal inference concept that you are familiar with.
- Write a blog post explaining the concept in a clear and concise way.
- Share your blog post with others and ask for feedback.
Participate in a causal inference competition
Show steps
Participating in a competition can help you to test your skills and learn from others.
Show steps
Show steps
Show steps
- Find a causal inference competition that you are interested in.
- Form a team or work on your own.
- Develop a solution to the problem.
- Submit your solution and compete for prizes.
Career center
Learners who complete Measuring Causal Effects in the Social Sciences will develop knowledge and skills
that may be useful to these careers:
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. This course Measuring Causal Effects in the Social Sciences, is an excellent fit for Economists as it will provide training in causal inference, which is essential for understanding the effects of economic policies and interventions. The course will also provide training in quantitative data analysis, which is necessary for testing economic theories and hypotheses.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses and organizations make informed decisions. This course Measuring Causal Effects in the Social Sciences, is a good fit for Statisticians because it will provide training in causal inference, which is essential for understanding the causal relationships between variables. The course will also provide training in quantitative data analysis, which is necessary for analyzing data and drawing valid conclusions.
Sociologist
Sociologist
Sociologists study society and social behavior by examining the interactions between individuals, groups, and institutions. This course Measuring Causal Effects in the Social Sciences, is a good fit for Sociologists because it will provide training in causal inference, which is essential for understanding the causes of social phenomena. The course will also provide training in quantitative data analysis, which is necessary for testing social theories and hypotheses.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states or events in specified populations. This course Measuring Causal Effects in the Social Sciences, is a good fit for Epidemiologists because it will provide training in causal inference, which is essential for understanding the causes of diseases and other health outcomes. The course will also provide training in quantitative data analysis, which is necessary for analyzing epidemiological data.
Academic Researcher
Academic Researcher
Academic Researchers conduct research to advance knowledge in their field. This course Measuring Causal Effects in the Social Sciences, is a good fit for Academic Researchers because it will provide training in causal inference, which is essential for understanding the causes of phenomena. The course will also provide training in quantitative data analysis, which is necessary for analyzing data and drawing valid conclusions.
Public Health Researcher
Public Health Researcher
Public Health Researchers study the determinants of health and disease in populations and develop and evaluate public health interventions. This course Measuring Causal Effects in the Social Sciences, is a good fit for Public Health Researchers because it will provide training in causal inference, which is essential for understanding the causes of health problems and evaluating the effectiveness of interventions. The course will also provide training in quantitative data analysis, which is necessary for analyzing public health data.
Political Scientist
Political Scientist
Political Scientists study the theory and practice of government and politics. This course Measuring Causal Effects in the Social Sciences, is a good fit for Political Scientists because it will provide training in causal inference, which is essential for understanding the causes of political phenomena. The course will also provide training in quantitative data analysis, which is necessary for testing political theories and hypotheses.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make predictions. This course Measuring Causal Effects in the Social Sciences, is a good fit for Quantitative Analysts because it will provide training in causal inference, which is essential for understanding the causal relationships between variables. The course will also provide training in quantitative data analysis, which is necessary for building predictive models.
Survey Researcher
Survey Researcher
Survey Researchers design and conduct surveys to collect data from populations. This course Measuring Causal Effects in the Social Sciences, is a good fit for Survey Researchers because it will provide training in causal inference, which is essential for understanding the causes of the relationships between variables. The course will also provide training in quantitative data analysis, which is necessary for analyzing survey data.
Program Evaluator
Program Evaluator
Program Evaluators assess the effectiveness of social programs and interventions. This course Measuring Causal Effects in the Social Sciences, is a good fit for Program Evaluators because it will provide training in causal inference, which is essential for understanding the effects of social programs. The course will also provide training in quantitative data analysis, which is necessary for evaluating the effectiveness of programs.
Data Scientist
Data Scientist
Data Scientists use statistical and machine learning techniques to extract insights from data. This course Measuring Causal Effects in the Social Sciences, is a good fit for Data Scientists because it will provide training in causal inference, which is essential for understanding the causal relationships between variables. The course will also provide training in quantitative data analysis, which is necessary for building predictive models.
Research Scientist
Research Scientist
Research Scientists conduct research to develop new knowledge and technologies. This course Measuring Causal Effects in the Social Sciences, is a good fit for Research Scientists because it will provide training in causal inference, which is essential for understanding the causes of phenomena. The course will also provide training in quantitative data analysis, which is necessary for analyzing data and drawing valid conclusions.
Market Researcher
Market Researcher
Market Researchers study consumer behavior to help businesses understand their customers and make better marketing decisions. This course Measuring Causal Effects in the Social Sciences, may be useful to a Market Researcher as it will provide a foundation in causal inference, which is essential for understanding the effects of marketing campaigns. The course will also provide training in quantitative data analysis, which is necessary for analyzing market research data.
Policy Analyst
Policy Analyst
Policy Analysts examine administrative policies to determine their efficiency and effectiveness, and to develop new or modified policies to meet the evolving needs of government or business. This course Measuring Causal Effects in the Social Sciences, might be useful to a Policy Analyst as it will provide a foundation in causal inference, which is essential for understanding the effects of policies. The course will also provide training in quantitative data analysis, which is necessary for evaluating the effectiveness of policies.
Consultant
Consultant
Consultants provide advice to businesses and organizations on a variety of topics. This course Measuring Causal Effects in the Social Sciences, may be useful to a Consultant as it will provide a foundation in causal inference, which is essential for understanding the causes of business problems. The course will also provide training in quantitative data analysis, which is necessary for analyzing data and drawing valid conclusions.
For more career information including salaries, visit:
OpenCourser.com/course/kjefbd/measuring
Reading list
We've selected nine 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
Measuring Causal Effects in the Social Sciences.
Save
A highly regarded and popular textbook that provides a comprehensive introduction to econometrics, with a focus on causal inference. It is accessible to advanced undergraduates and graduate students, and provides a solid foundation for further study in causal inference.
Save
A highly regarded and popular textbook that provides a clear and concise overview of causal inference. It is accessible to a wide range of readers, from students to researchers, and valuable resource for anyone interested in understanding and applying causal inference methods.
Save
A landmark work on causal inference, which provides a unified framework for understanding and modeling causal relationships. It covers a wide range of topics, from the foundations of causality to advanced methods for causal inference.
A practical guide to interpretable machine learning methods, which are becoming increasingly important in causal inference. It provides a comprehensive overview of the field and includes numerous examples and exercises.
Save
A practical guide to causal impact analysis for policy evaluation. It covers a wide range of topics, from the basics of causal inference to advanced methods for impact evaluation. It valuable resource for researchers and policy makers alike.
A concise introduction to causal inference from a probabilistic perspective. It covers the fundamental concepts of causality, such as counterfactuals and graphical models, and provides a solid foundation for further study in the field.
A comprehensive handbook on causal analysis methods for social research. It covers a wide range of topics, from basic concepts to advanced methods, and provides numerous examples and case studies.
Save
A clear and concise introduction to systems thinking, which valuable tool for understanding and analyzing complex causal relationships. It useful supplement to causal inference courses and can help students to develop a more holistic understanding of the world.
A classic work on the philosophy of science, which provides a rigorous framework for understanding and evaluating scientific theories. It is essential reading for anyone interested in the foundations of causal inference.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kjefbd/measuring
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Time
24 hours
Via
Coursera
Institution
University of Copenhagen
Instructor
Anders Holm
Good to know
Develops causal relationships using quantitative data and social sciences
Taught by Anders Holm, a recognized expert in causal inference
Uses a PhD-level approach to causal analysis, making it suitable for advanced students
Examines real-world examples of wage discrimination, offering practical insights
Builds a strong foundation in causal inference for those interested in research or policy analysis
Requires familiarity with quantitative methods and a solid understanding of statistics
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