This is the final item in my series on Matching Algorithms and Reproductive Health, and it brings the story full circle, returning to the algorithms side of the show. Today I’ll demonstrate how to actually find minimum-weight perfect matchings in Python, and toss in a little story about . Continue reading
Tag Archives: matching algorithms
ACO in Python: Minimum Weight Perfect Matchings (a.k.a. Matching Algorithms and Reproductive Health: Part 4)
It’s been three weeks and one IHME retreat since I wrote about matching algorithms and virginity pledges, and I think I now understand what’s going on in Patient Teenagers well enough to describe it. I’ll try to give a stylized example of how the minimum-weight perfect matching algorithm makes itself useful in reproductive health research.
I think it’s helpful to focus on a concrete research question about the virginity pledge and its effects on reproductive health. Here’s one: “does taking the pledge reduce the chances that an individual contracts trichomoniasis?” If the answer is yes, or if the answer is no, people can still argue about the value of the virginity pledge programs, but this seems like relevant information for decision making. Continue reading
I might have been a little over-ambitious with this series. I wrote a little bit about the how matching theory emerged from the social sciences two weeks ago. But then I got really busy! And that was the part I actually knew something about ahead of time. The promised connection between matching algorithms and reproductive health (and more generally, how matching is being used in quasi-experiment design) is the part that I have to do some reading on before I can write knowledgeably about.
However, I have a plan: I’d like to “crowd-source” my library research. Continue reading
Earlier this week, I was inspired by current events to launch a bold, crazy-sounding series about matching theory and its application to reproductive health. This first installment is a quick social history of the development of matching theory, largely influenced by (and fact-checked against) Lex Scrijver’s encyclopediac Combinatorial Optimization: Polyhedra and Efficiency. His paper “On the history of combinatorial optimization (till 1960)” contains similar information in an easy-to-download form.
On to the story: how social science applications drove the development of matching theory. Continue reading
One of the first things on Obama’s agenda after being sworn in as President last week was lifting the “global gag rule”, a Regan-era innovation that tied US aid to strict anti-choice regulations. Meanwhile, the TCS reading group at UW has been studying matching problems and Edmond’s blossom algorithm. Together, this has been the motivation I needed to launch a series of posts about applications of matchings in reproductive health metrics. Part 1 will have more about matchings.