Applying targeted learning to improve global health equity

This post is part of our Q&A series.

A question from graduate students in our Spring 2021 offering of the new course “Targeted Learning in Practice” at UC Berkeley:


Hi Mark,

A pressing issue in the field of global public health is equitable ownership of data and results in terms of both authorship and representation. In some aspects, targeted learning improves equity by bolstering our ability to efficiently draw causal inferences from global health data. On the other hand, targeted learning and other advanced analytic methods can decrease equity by increasing data extraction from low- and middle-income countries by researchers from high-income countries and further obscuring the research process through complexity. What steps can and should we take to ensure that biostatistical methods are applied in a way to maximize equity? Throughout the semester, we have covered a few examples of applications of these methods using minimal specification adjustment (e.g., sl3 Microwave Dinner implementation” and “Easy-Bake tmle3), but would you discourage the applications of these methods without an understanding of the underlying statistical theory?




Hi Z.B.,

Thank you for the interesting question.

Yes, it is a fair point that data science has a huge impact on society and the world as a whole and is already causing enormous equity issues, e.g., potential censorship and/or manipulation of whole societies (as in Orwell’s 1984), beyond other issues. Targeted learning requires transparent formulation of the estimation problem to be solved, and then lets the science and an a priori defined machine carry out the analysis. In this way, the conclusions are objective and not driven by human or other biases. This could be used to solve equity problems itself, such as fairness constraints in prediction or causal effects of actions on equity-type outcomes. This is already happening, and certainly represents an important class of problems that are receiving attention, ones in which TMLE should play an important role. I would think that with increasingly open access to data and the more we can utilize data from low- and middle-income countries (transparently and out-in-the-open), the more one will require a priori-specified reproducible methods, and the better these equity issues can be addressed so that one can shed light on important developments.

In addition, it is important that people spend time and resources to study these issues, define them objectively, and show what the data can tell us with such transparent reproducible methods.

I certainly share your concern.

Best Wishes,


P.S., remember to write in to our blog at vanderlaan (DOT) blog [AT] berkeley (DOT) edu. Interesting questions will be answered on our blog!

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