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Vinod Bakthavachalam
Data scientists often get asked questions related to causality: (1) did recent PR coverage drive sign-ups, (2) does customer support increase sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn techniques that can answer questions like these, which are centered around issues of causality and are solved with causal inference.
Read more
Data scientists often get asked questions related to causality: (1) did recent PR coverage drive sign-ups, (2) does customer support increase sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn techniques that can answer questions like these, which are centered around issues of causality and are solved with causal inference.
Read more
Data scientists often get asked questions related to causality: (1) did recent PR coverage drive sign-ups, (2) does customer support increase sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn techniques that can answer questions like these, which are centered around issues of causality and are solved with causal inference.
In this project, you will learn the high level theory and intuition behind the four main causal inference techniques of controlled regression, regression discontinuity, difference in difference, and instrumental variables as well as some techniques at the intersection of machine learning and causal inference that are useful in data science called double selection and causal forests. These will help you rigorously answer questions like those above and become a better data scientist!
Register for this course and see more details by visiting:
OpenCourser.com/course/8ctnm5/essential
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Syllabus
Essential Causal Inference Techniques for Data Science
In this project, you will learn the high level theory and intuition behind the four main causal inference techniques of controlled regression, regression discontinuity, difference in difference, and instrumental variables as well as some techniques at the intersection of machine learning and causal inference that are useful in data science. The practice sessions will run you through real world examples that teach how to fit each causal inference technique in R as well.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces causal inference, a cornerstone of data science, to drive business decisions
Covers various causal inference techniques including regression discontinuity and instrumental variables
Focuses on practical applications of causal inference techniques in data science
Incorporates machine learning techniques to enhance causal inference analysis
Emphasizes real-world examples to illustrate the use of causal inference techniques
Provides hands-on practice sessions to reinforce learning
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Save Essential Causal Inference Techniques for Data Science to your list so you can find it easily later:
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Reviews summary
Basic causal inference foundation
According to students, this course serves as a solid introduction to the foundations of causal inference. They say that lectures align well with assessments which are not extremely difficult. Although there is some content missing that students wish was included, such as use of time-series data, they still gained practical experience with key concepts such as generating code.
Lectures map to assessments.
"Good match between lecture/example and tests."
Exercises involve the generation of causal inference code.
"Decent start to Causal Inference Techniques with sufficient theory for a project. "
Essential concepts are not covered.
"Delivering the promised essential with adequate value. "
"it would also be useful to see more on the causal forest, which is the area which interested me in particular"
"Would like to have discussed more practical ways to apply these methods to machine learning and when-to-use-which technique"
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 Essential Causal Inference Techniques for Data Science with these
activities:
Refresh basics of regression analysis
Show steps
Refreshes the prerequisites of regression analysis, which will enable you to follow the causal inference techniques better.
Show steps
-
Review the concepts of simple and multiple regression analysis, including the assumptions and interpretation of results.
-
Practice applying regression models to real-world data.
Practice exercises on controlled regression
Show steps
Provides hands-on experience with the first of the causal inference techniques covered in the course.
Show steps
-
Work through examples and exercises on controlled regression.
-
Implement controlled regression models in R.
-
Interpret and evaluate the results of controlled regression models.
Practice exercises on regression discontinuity design
Show steps
Provides hands-on experience with the second of the causal inference techniques covered in the course.
Show steps
-
Work through examples and exercises on regression discontinuity design.
-
Implement regression discontinuity design models in R.
-
Interpret and evaluate the results of regression discontinuity design models.
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice exercises on difference-in-differences
Show steps
Provides hands-on experience with the third of the causal inference techniques covered in the course.
Show steps
-
Work through examples and exercises on difference-in-differences.
-
Implement difference-in-differences models in R.
-
Interpret and evaluate the results of difference-in-differences models.
Practice exercises on instrumental variables
Show steps
Provides hands-on experience with the fourth of the causal inference techniques covered in the course.
Browse courses on
Instrumental Variables
Show steps
-
Work through examples and exercises on instrumental variables.
-
Implement instrumental variables models in R.
-
Interpret and evaluate the results of instrumental variables models.
Refresh basics of regression analysis
Show steps
Refreshes the prerequisites of regression analysis, which will enable you to follow the causal inference techniques better.
Show steps
Show steps
Show steps
- Review the concepts of simple and multiple regression analysis, including the assumptions and interpretation of results.
- Practice applying regression models to real-world data.
Practice exercises on controlled regression
Show steps
Provides hands-on experience with the first of the causal inference techniques covered in the course.
Show steps
Show steps
Show steps
- Work through examples and exercises on controlled regression.
- Implement controlled regression models in R.
- Interpret and evaluate the results of controlled regression models.
Practice exercises on regression discontinuity design
Show steps
Provides hands-on experience with the second of the causal inference techniques covered in the course.
Show steps
Show steps
Show steps
- Work through examples and exercises on regression discontinuity design.
- Implement regression discontinuity design models in R.
- Interpret and evaluate the results of regression discontinuity design models.
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice exercises on difference-in-differences
Show steps
Provides hands-on experience with the third of the causal inference techniques covered in the course.
Show steps
Show steps
Show steps
- Work through examples and exercises on difference-in-differences.
- Implement difference-in-differences models in R.
- Interpret and evaluate the results of difference-in-differences models.
Practice exercises on instrumental variables
Show steps
Provides hands-on experience with the fourth of the causal inference techniques covered in the course.
Browse courses on
Instrumental Variables
Show steps
Show steps
Show steps
- Work through examples and exercises on instrumental variables.
- Implement instrumental variables models in R.
- Interpret and evaluate the results of instrumental variables models.
Career center
Learners who complete Essential Causal Inference Techniques for Data Science will develop knowledge and skills
that may be useful to these careers:
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of disease and other health conditions in populations. This course will equip you with a solid understanding of causal inference methods, which are crucial for identifying risk factors and developing effective public health interventions. The knowledge gained from this course will enhance your ability to conduct epidemiological studies and contribute to the improvement of public health outcomes.
Data Scientist
Data Scientist
A Data Scientist is responsible for extracting insights from data using machine learning, statistics, and other advanced analytical techniques. This course's focus on causal inference techniques for data science will provide you with the necessary skills and knowledge to identify and quantify the causal effects of different variables in complex datasets. This knowledge will be highly valuable in your role as a Data Scientist, enabling you to make informed decisions based on evidence and contribute to the development of effective data-driven solutions.
Statistician
Statistician
A Statistician designs and conducts statistical studies to collect, analyze, and interpret data. This course will provide you with a strong foundation in causal inference techniques, which are essential for understanding the relationship between variables and making valid conclusions from data. The knowledge and skills gained from this course will enhance your ability to design robust statistical studies and contribute to meaningful research in various fields.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to medical research. This course will provide you with a strong foundation in causal inference techniques, which are essential for understanding the relationship between medical interventions and health outcomes. The knowledge and skills gained from this course will enable you to design and conduct robust clinical studies and contribute to the advancement of medical research.
Data Analyst
Data Analyst
A Data Analyst cleans, analyzes, and interprets data to extract insights and solve business problems. This course will teach you a range of causal inference techniques that are highly valuable in data analysis. By understanding how to identify and quantify causal effects, you will be able to draw more accurate conclusions from data and provide more actionable insights to stakeholders.
Marketing Analyst
Marketing Analyst
A Marketing Analyst designs and conducts research to understand consumer behavior and market trends. This course will provide you with a comprehensive understanding of causal inference techniques, which will empower you to evaluate the effectiveness of marketing campaigns and make data-driven decisions. By understanding how to isolate and measure the impact of different marketing strategies, you can optimize your campaigns for better results.
Economist
Economist
An Economist analyzes economic data to understand economic trends and make predictions. This course will provide you with a solides foundation in causal inference techniques, which are essential for understanding the relationship between economic variables and making accurate forecasts. The knowledge and skills gained from this course will enhance your ability to conduct economic research and contribute to the development of sound economic policies.
Policy Analyst
Policy Analyst
A Policy Analyst researches and analyzes public policies to inform decision-making. This course's focus on causal inference techniques will equip you with the necessary skills to evaluate the impact of different policies and make evidence-based recommendations. The knowledge gained from this course will enhance your ability to conduct policy research and contribute to the development of effective public policies.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst (Quant) uses mathematical and statistical models to analyze financial data and make investment decisions. This course introduces you to a range of causal inference techniques commonly used in the financial industry. By understanding how to identify and quantify causal effects, you will be able to build more accurate and reliable models and make more informed investment decisions.
Actuary
Actuary
An Actuary evaluates and manages financial risks using mathematical and statistical models. This course will provide you with a comprehensive understanding of causal inference techniques frequently used in the insurance industry. By understanding how to identify and quantify causal effects, you will be able to develop more accurate and reliable models and make more informed risk management decisions.
Market Researcher
Market Researcher
A Market Researcher conducts research to understand consumer behavior and market trends. This course will provide you with a foundational understanding of causal inference techniques, which will empower you to evaluate the effectiveness of marketing campaigns and make data-driven decisions. By understanding how to isolate and measure the impact of different marketing strategies, you can optimize your campaigns for better results.
Consultant
Consultant
A Consultant provides advice and guidance to businesses on a variety of topics. This course may be useful for Consultants who are interested in leveraging data to support their consulting clients and provide more valuable insights. The knowledge and skills gained from this course can help you understand the causal impact of different business decisions and make more informed recommendations to your clients.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. This course may be useful for Product Managers who are interested in leveraging data to drive product decisions and improve product outcomes. The knowledge and skills gained from this course can help you understand the causal impact of different product features and make more informed decisions about product development.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course may be useful for Software Engineers who are interested in building data-driven applications and developing software solutions that leverage causal inference techniques. The knowledge and skills gained from this course can help you create more robust and reliable software systems.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and identifies opportunities for improvement. This course may be useful for Business Analysts who are interested in leveraging data to drive business decisions and improve business outcomes. The knowledge and skills gained from this course can help you understand the causal impact of different business strategies and make more informed decisions about business operations.
For more career information including salaries, visit:
OpenCourser.com/course/8ctnm5/essential
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
Essential Causal Inference Techniques for Data Science.
Provides a comprehensive introduction to causal inference, covering the essential concepts and techniques. It valuable resource for anyone interested in learning more about causal inference.
An important reference book for econometrics. It is well-organized and full of examples.
Save
Provides a more advanced treatment of causal inference, covering more complex topics such as mediation analysis and instrumental variables. It valuable resource for researchers who want to learn more about causal inference.
This reference book provides an in-depth account of causal inference from a statistical perspective.
Provides an overview of causal inference methods for social and economic data, covering topics such as regression discontinuity design, difference-in-differences, and instrumental variables. It valuable resource for researchers who want to learn more about causal inference in these fields.
Save
Provides an overview of statistical methods for social research, including causal inference methods such as regression discontinuity design and instrumental variables. It valuable resource for researchers who want to learn more about causal inference in this field.
Save
Provides a popular introduction to causal inference. It valuable resource for anyone who wants to learn more about the topic without getting bogged down in the math.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8ctnm5/essential
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Vinod Bakthavachalam
Language
English
Good to know
Introduces causal inference, a cornerstone of data science, to drive business decisions
Covers various causal inference techniques including regression discontinuity and instrumental variables
Focuses on practical applications of causal inference techniques in data science
Incorporates machine learning techniques to enhance causal inference analysis
Emphasizes real-world examples to illustrate the use of causal inference techniques
Provides hands-on practice sessions to reinforce learning
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