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Thomas Woolf
This is the fourth course in the specialization and is aimed at those with basic knowledge of statistics, probability and linear algebra. It will prove to be especially interesting for those with datasets that are being used to make decisions: either business, medical, or technology based.
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What's inside
Syllabus
Sequential Causal Decisions
How do we best use information for setting polices, for setting prices, and for deciding what actions to take? We've been exploring this throughout our specialization. In this fourth course, we add in an intriguing route for optimizing decisions: causality. We start with considerations of a causal graphical structure, when it can be cleanly defined, where we have the ability to make decisions, to take actions, and to evaluate overall policies with a clear eye towards how the individual decisions are causally linked to the outcome. This is not always possible, but the ability to think about setting up this type of decision making structure is the end goal of our specialization. It is only possible with sufficient data, and that data does need to have a particular structure, ideally even having been collected with a causal analysis as the end point. In this first week we evaluate how to best update prices for a supermarket, as an example of the type of decision making that may be possible with these methods.
Read more
Syllabus
Sequential Causal Decisions
How do we best use information for setting polices, for setting prices, and for deciding what actions to take? We've been exploring this throughout our specialization. In this fourth course, we add in an intriguing route for optimizing decisions: causality. We start with considerations of a causal graphical structure, when it can be cleanly defined, where we have the ability to make decisions, to take actions, and to evaluate overall policies with a clear eye towards how the individual decisions are causally linked to the outcome. This is not always possible, but the ability to think about setting up this type of decision making structure is the end goal of our specialization. It is only possible with sufficient data, and that data does need to have a particular structure, ideally even having been collected with a causal analysis as the end point. In this first week we evaluate how to best update prices for a supermarket, as an example of the type of decision making that may be possible with these methods.
Read more
Is that a Causal Decision or a Causal Effect?
Classification and Regression are the lodestars of statistics and they come up again within the context of causal decisions. In this case the regression is really using the data to attempt to determine causal effects. In an analog to classification, when we define the optimal causal decision we are optimizing in a different way on the data than we do within the causal effect. This helps us to decide what we most want to determine from the data and what are the most important goals from the analysis. In this second week we continue our story for optimizing causal decisions by focusing on where best to locate a restaurant based on location data (anonymized) from cell phone user data.
Causal Random Forests
Online advertising is both a challenge and an opportunity for businesses. From the marketing perspective, defining how much to spend and on who and when is the question they need answered. From a user perspective, the fewer the ads the better, but if there must be ads, can they be relevant and helpful. This is an example of a marketplace, based on online advertising. A simple example, one that we considered earlier, is A:B testing. In this week we return to this question of how to optimize the presentation of information, but now from a causal perspective. We will go over the causal forest route for how to determine an optimal causal decision. We will also go over randomized clinical trials and how the control of the treatment effect can make the analysis significantly cleaner.
Blessings of Multiple Causes
David Blei's group contributed an intriguing route for defining causal decisions where confounding is still present, but can be treated in a way that still determines a causal understanding of the decision process. We will explore that paper, and its implications, along with other similar routes for working with data where the confounding issues may otherwise make the analysis a challenge. We open with how best to define the spend on a new movie project: hire that star actor or spend more on stunts? This is another example of a causally linked decision that does not have a final 'right' or 'wrong' but that does have major implications for a business.
Individual Treatment Effects and Personalized Medicine
How best to determine a decision for a large group versus an individual is our final motif for the specialization. In many ways this is the defining question for healthcare: how best to determine an optimal treatment for each individual? While there is a lot of work ahead to describe how this may be done in a detailed setting, there has been progress towards how to use information collected for many individuals that can then be a help in defining the best treatment for an individual. It is this current progress that has many people excited for the future of personalized medicine. At the same time it is a appropriate to realize the limits of what can be done, right now, with the question of an optimal individual treatment.
Untitled Module
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores causal decisions, which is standard in business and medical decision making
Taught by Thomas Woolf, who are recognized for their work in causality
Develops an understanding of causal decision making, which is a core skill for data analysis
Examines causal decisions, which is critical in fields such as business and technology
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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 Data Science Decisions in Time: Using Causal Information with these
activities:
Seek Mentorship from a Causal Inference Expert
Show steps
Connects learners with experienced professionals who can provide guidance, support, and industry insights in causal inference.
Browse courses on
Causal Inference
Show steps
-
Attend conferences and workshops related to causal inference.
-
Reach out to researchers or practitioners in the field.
-
Explain your goals and interests.
-
Establish a mentoring relationship, defining expectations and communication channels.
-
Regularly meet with the mentor for guidance and feedback.
Compile a Glossary of Causal Inference Terms
Show steps
Enhances understanding of causal inference concepts and terminology by creating a personalized reference.
Browse courses on
Causal Inference
Show steps
-
Review course materials and textbooks.
-
Search for additional resources and definitions.
-
Create a list of important terms.
-
Define each term clearly and concisely.
-
Organize the glossary alphabetically or by topic.
Review Statistical Methods for Causality
Show steps
Examines methods for drawing causal inferences from observational studies using causal graphs, structural equations, potential outcomes, and counterfactuals.
View
Causal Inference
on Amazon
Show steps
-
Read the book's abstract and preface.
-
Review the table of contents.
-
Skim the chapters related to the course.
-
Highlight important concepts and terms.
-
Complete the exercises at the end of each chapter (optional).
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a Workshop on Causal Inference Applications
Show steps
Provides an immersive learning experience where participants learn from experts and apply causal inference techniques to real-world case studies.
Browse courses on
Causal Inference
Show steps
-
Identify and register for relevant workshops.
-
Attend the workshop and actively participate.
-
Take notes and ask questions.
-
Apply the learned techniques in projects or assignments.
-
Network with other participants and professionals.
Explore Causal Inference with Python Libraries
Show steps
Leverages Python libraries to implement causal inference methods and gain hands-on experience analyzing causal relationships in data.
Browse courses on
Causal Inference
Show steps
-
Install necessary Python libraries.
-
Follow tutorials on using libraries like因果关系推断, DoWhy, and EconML.
-
Load and preprocess data.
-
Apply causal inference methods and interpret results.
-
Visualize and communicate findings.
Create a Causal Graph for a Business Problem
Show steps
Develops a graphical representation of a business problem to identify causal relationships and potential interventions for decision-making.
Browse courses on
Causal Inference
Show steps
-
Identify the problem or question.
-
List the variables involved.
-
Draw arrows to represent causal relationships, using arrows to show causality.
-
Check for directed cycles and adjust the graph accordingly.
-
Use the graph to identify potential interventions and make decisions.
Participate in a Causal Inference Competition
Show steps
Engages in a competitive environment to refine causal inference skills, test knowledge, and gain recognition for outstanding performance.
Browse courses on
Causal Inference
Show steps
-
Find and register for relevant competitions.
-
Form a team or work individually.
-
Analyze the provided data and develop causal models.
-
Submit solutions and document findings.
-
Review results and learn from other participants.
Solve Causal Inference Problems
Show steps
Sharpens the ability to apply causal inference techniques to real-world problems and evaluate the validity of causal claims.
Browse courses on
Causal Inference
Show steps
-
Identify the problem and the causal question.
-
Check for confounding variables.
-
Apply appropriate statistical tests or methods.
-
Interpret the results and draw conclusions.
-
Consider alternative explanations and sensitivity analyses.
Seek Mentorship from a Causal Inference Expert
Show steps
Connects learners with experienced professionals who can provide guidance, support, and industry insights in causal inference.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Attend conferences and workshops related to causal inference.
- Reach out to researchers or practitioners in the field.
- Explain your goals and interests.
- Establish a mentoring relationship, defining expectations and communication channels.
- Regularly meet with the mentor for guidance and feedback.
Compile a Glossary of Causal Inference Terms
Show steps
Enhances understanding of causal inference concepts and terminology by creating a personalized reference.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Review course materials and textbooks.
- Search for additional resources and definitions.
- Create a list of important terms.
- Define each term clearly and concisely.
- Organize the glossary alphabetically or by topic.
Review Statistical Methods for Causality
Show steps
Examines methods for drawing causal inferences from observational studies using causal graphs, structural equations, potential outcomes, and counterfactuals.
View
Causal Inference
on Amazon
Show steps
Show steps
Show steps
- Read the book's abstract and preface.
- Review the table of contents.
- Skim the chapters related to the course.
- Highlight important concepts and terms.
- Complete the exercises at the end of each chapter (optional).
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a Workshop on Causal Inference Applications
Show steps
Provides an immersive learning experience where participants learn from experts and apply causal inference techniques to real-world case studies.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Identify and register for relevant workshops.
- Attend the workshop and actively participate.
- Take notes and ask questions.
- Apply the learned techniques in projects or assignments.
- Network with other participants and professionals.
Explore Causal Inference with Python Libraries
Show steps
Leverages Python libraries to implement causal inference methods and gain hands-on experience analyzing causal relationships in data.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Install necessary Python libraries.
- Follow tutorials on using libraries like因果关系推断, DoWhy, and EconML.
- Load and preprocess data.
- Apply causal inference methods and interpret results.
- Visualize and communicate findings.
Create a Causal Graph for a Business Problem
Show steps
Develops a graphical representation of a business problem to identify causal relationships and potential interventions for decision-making.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Identify the problem or question.
- List the variables involved.
- Draw arrows to represent causal relationships, using arrows to show causality.
- Check for directed cycles and adjust the graph accordingly.
- Use the graph to identify potential interventions and make decisions.
Participate in a Causal Inference Competition
Show steps
Engages in a competitive environment to refine causal inference skills, test knowledge, and gain recognition for outstanding performance.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Find and register for relevant competitions.
- Form a team or work individually.
- Analyze the provided data and develop causal models.
- Submit solutions and document findings.
- Review results and learn from other participants.
Solve Causal Inference Problems
Show steps
Sharpens the ability to apply causal inference techniques to real-world problems and evaluate the validity of causal claims.
Browse courses on
Causal Inference
Show steps
Show steps
Show steps
- Identify the problem and the causal question.
- Check for confounding variables.
- Apply appropriate statistical tests or methods.
- Interpret the results and draw conclusions.
- Consider alternative explanations and sensitivity analyses.
Career center
Learners who complete Data Science Decisions in Time: Using Causal Information will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/qmxeay/data
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Effort
3-5 hours/week
Level
Intermediate
Via
Coursera
Institution
Johns Hopkins University
Instructor
Thomas Woolf
Session
August 7, 2024
- September 30, 2024
Language
English
Good to know
Explores causal decisions, which is standard in business and medical decision making
Taught by Thomas Woolf, who are recognized for their work in causality
Develops an understanding of causal decision making, which is a core skill for data analysis
Examines causal decisions, which is critical in fields such as business and technology
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