PIRSA:19010074

Video URL

https://pirsa.org/19010074

Mind Over Data: One Thing You Know that Machines (and some Statisticians) Don’t

MacKenzie, D. (2019). Mind Over Data: One Thing You Know that Machines (and some Statisticians) Don’t. Perimeter Institute for Theoretical Physics. https://pirsa.org/19010074

MacKenzie, Dana. Mind Over Data: One Thing You Know that Machines (and some Statisticians) Don’t. Perimeter Institute for Theoretical Physics, Jan. 30, 2019, https://pirsa.org/19010074 

          @misc{ scivideos_PIRSA:19010074,
            doi = {10.48660/19010074},
            url = {https://pirsa.org/19010074},
            author = {MacKenzie, Dana},
            keywords = {Other Physics},
            language = {en},
            title = {Mind Over Data: One Thing You Know that Machines (and some Statisticians) Don{\textquoteright}t},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2019},
            month = {jan},
            note = {PIRSA:19010074 see, \url{https://scivideos.org/pirsa/19010074}}
          }

Dana MacKenzie Freelance Writer

Talk numberPIRSA:19010074
DOI10.48660/19010074
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

From earliest infancy, we live in and learn to function in a world of causes and effects. Yet science has had an ambivalent, even hostile attitude toward causation for more than a century. Statistics courses teach us that “correlation is not causation,” yet they are strangely silent about what is causation.

A central reason for this silence is that causation does not reside in data alone, but in the process that generates the data. In order to answer causal questions, like “What would happen if we lowered the price of toothpaste?” or “Should I brake for this object?” we need a model of causes and effects. Judea Pearl has developed a simple calculus for expressing our cause-effect knowledge in a diagram and using that diagram to tell us how to interpret the data we gather from the real world. His methods are already transforming the practice of statistics and could equip future artificial intelligences with causal reasoning abilities they currently lack.

This talk is largely based on Mackenzie’s book co-written with Pearl, The Book of Why.