January 29, 2020

"Data science methods to reduce inequality and improve healthcare" (Also Next Week at the Statistics Seminar)

Attention conservation notice: Announcement of a highly technical talk, in a subject you're not interested in, at a university you're not near.

Emma Pierson, "Data science methods to reduce inequality and improve healthcare"
Abstract: I will describe how to use data science methods to understand and reduce inequality in two domains: criminal justice and healthcare. First, I will discuss how to use Bayesian modeling to detect racial discrimination in policing. Second, I will describe how to use machine learning to explain racial and socioeconomic inequality in pain.
Time and place: 4--5 pm on Wednesday, 5 February 2020, in Hamerschlag Hall B103

As always, talks are free and open to the public.

Enigmas of Chance; Commit a Social Science

Posted at January 29, 2020 13:45 | permanent link

January 28, 2020

"The Blessings of Multiple Causes" (Next Week at the Statistics Seminar)

Attention conservation notice: Announcement of a highly technical talk, in a subject you're not interested in, at a university you're not near.

I am very much looking forward to our first talk of the semester, which I am sure will evoke lively discussion:

Yixing Wang, "The Blessings of Multiple Causes"
Abstract: Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods assume that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal inference from observational data allowing for unobserved confounding.
How does the deconfounder work? The deconfounder is designed for problems of multiple causal inferences: scientific studies that involve many causes whose effects are simultaneously of interest. The deconfounder uses the correlation among causes as evidence for unobserved confounders, combining unsupervised machine learning and predictive model checking to perform causal inference. We study the theoretical requirements for the deconfounder to provide unbiased causal estimates, along with its limitations and tradeoffs. We demonstrate the deconfounder on real-world data and simulation studies.
Time and place: 4--5 pm on Monday, 3 February 2020, in Hamerschlag Hall B103

As always, talks are free and open to the public.

Enigmas of Chance; Constant Conjunction Necessary Connexion

Posted at January 28, 2020 12:06 | permanent link

Three-Toed Sloth