Sunday, June 15, 2014

Big Ag and Big Data | Marc F. Bellemare

A very good post about big data in general and applications in agriculture specifically by Marc Bellemere can be found here:

He clears up a misconception that I've talked about before, where some gainsay big data because it doesn't solve all of the fundamental issues of causal inference.

The promises of big data were never about causal inference. The promise of big data is prediction:

"There is a fundamental difference between estimating causal relationships and forecasting. The former requires a research design in which X is plausibly exogenous to Y. The latter only requires that X include as much stuff as possible."

"When it comes to forecasting, big data is unbeatable. With an ever larger number of observations and variables, it should become very easy to forecast all kinds of things …"
"But when it comes to doing science, big data is dumb. It is only when we think carefully about the research design required to answer the question "Does X cause Y?" that we know which data to collect, and how much of them. The trend in the social sciences over the last 20 years has been toward identifying causal relationships, and away from observational data — big or not."
He goes on to that end to discuss how big data is being leveraged in food production, and shares a point of enthusiasm that I think is reveals an important point that I have made before regarding the convergence of big data, technology, and genomics

"This is exactly the kind of innovation that makes me so optimistic about the future of food and that makes me think the neo-Malthusians, just like the Malthusians of old, are wrong."

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