Hugo speaks with Jim Savage, the Director of Data Science at Schmidt Futures, about the need for data science in executive training and decision, what data scientists can learn from economists, the perils of "data for good", and why you should always be integrating your loss function over your posterior.
Jim and Hugo talk about what data science is and isn’t capable of, what can actually deliver value, and what people really enjoy doing: the intersection in this Venn diagram is where we need to focus energy and it may not be quite what you think it is!
They then dive into Jim's thoughts on what he dubs Executive Data Science. You may be aware of the slicing of the data science and machine learning spaces into descriptive analytics, predictive analytics, and prescriptive analytics but, being the thought surgeon that he is, Jim proposes a different slicing into
(1) tool building OR data science as a product,
(2) tools to automate and augment parts of us, and
(3) what Jim calls Executive Data Science.
Jim and Hugo also talk about decision theory, the woeful state of causal inference techniques in contemporary data science, and what techniques it would behoove us all to import from econometrics and economics, more generally. If that’s not enough, they talk about the importance of thinking through the data generating process and things that can go wrong if you don’t. In terms of allowing your data work to inform your decision making, thery also discuss Jim’s maxim “ALWAYS BE INTEGRATING YOUR LOSS FUNCTION OVER YOUR POSTERIOR”
Last but definitively not least, as Jim has worked in the data for good space for much of his career, they talk about what this actually means, with particular reference to fast.ai founder & QUT professor of practice Rachel Thomas’ blog post called “Doing Data Science for Social Good, Responsibly” (https://www.fast.ai/2021/11/23/data-for-good/). Rachel’s post takes as its starting point the following words of Sarah Hooker, a researcher at Google Brain:
"Data for good" is an imprecise term that says little about who we serve, the tools used, or the goals. Being more precise can help us be more accountable & have a greater positive impact.
And Jim and I discuss his work in the light of these foundational considerations.
Links
Jim on twitter (https://twitter.com/abiylfoyp/)
What Is Causal Inference?An Introduction for Data Scientists (https://www.oreilly.com/radar/what-is-causal-inference/) by Hugo Bowne-Anderson and Mike Loukides
Jim's must-watch Data Council talk on Productizing Structural Models (https://www.datacouncil.ai/talks/productizing-structural-models)
[Mastering Metrics}(https://www.masteringmetrics.com/) by Angrist and Pischke
Mostly Harmless Econometrics: An Empiricist's Companion (https://press.princeton.edu/books/paperback/9780691120355/mostly-harmless-econometrics) by Angrist and Pischke
The Book of Why (https://en.wikipedia.org/wiki/The_Book_of_Why) by Judea Pearl
Decision-Making in a Time of Crisis (https://www.oreilly.com/radar/decision-making-in-a-time-of-crisis/) by Hugo Bowne-Anderson
Doing Data Science for Social Good, Responsibly (https://www.fast.ai/2021/11/23/data-for-good/) by Rachel Thomas

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