Data Science Decisions in Time

Using Causal Information

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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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.

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.

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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

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Connects learners with experienced professionals who can provide guidance, support, and industry insights in causal inference.

Browse courses on Causal Inference

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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

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Enhances understanding of causal inference concepts and terminology by creating a personalized reference.

Browse courses on Causal Inference

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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

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Examines methods for drawing causal inferences from observational studies using causal graphs, structural equations, potential outcomes, and counterfactuals.

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