Johns Hopkins University
Data Science Decisions in Time: Using Causal Information
Johns Hopkins University

Data Science Decisions in Time: Using Causal Information

Thomas Woolf

Instructor: Thomas Woolf

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

29 hours to complete
3 weeks at 9 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

29 hours to complete
3 weeks at 9 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Recently updated!

August 2024

Assessments

16 assignments

Taught in English

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This course is part of the Data Science Decisions in Time Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course

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.

What's included

3 videos1 reading3 assignments

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.

What's included

3 videos1 reading3 assignments

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.

What's included

3 videos1 reading3 assignments

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.

What's included

3 videos1 reading3 assignments

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.

What's included

3 videos1 reading3 assignments

What's included

1 assignment1 programming assignment

Instructor

Thomas Woolf
Johns Hopkins University
4 Courses383 learners

Offered by

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