Meetup : [Boston] Using Causal Graphs to Understand Bias in the Medical Literature
WHEN: 11 August 2013 02:00:00PM (-0400)
This talk is a very gentle introduction to modern causality theory, as developed by Jamie Robins and Judea Pearl. We will use as little mathematics as possible to introduce some of the central ideas in the field, with examples from medicine and epidemiology. In the one hour talk, we will cover:
(1) Causal Directed Acyclic Graphs and the rules of D-Separation
(2) Definitions of confounding and selection bias
(3) Methods to adjust for confounding
(4) Examples of situations where standard methods are always biased.
Cambridge/Boston-area Less Wrong meetups are every Sunday at 2pm in MIT’s building 66 at 25 Ames St, room 156. Room number subject to change based on availability; signs will be posted with the actual room number.
Our default schedule is as follows:
—Phase 1: Arrival, greetings, unstructured conversation.
—Phase 2: The headline event. This starts promptly at 2:30, and lasts 30-60 minutes.
—Phase 3: Further discussion. We’ll explore the ideas raised in phase 2, often in smaller groups.
—Phase 4: Dinner. It’s about a ten minute walk to the usual restaurant.
Any chance there will be a video of the talk? Sounds very interesting.
We’re not set up to do that. If there are slides, I’ll see if the speaker is willing to share them.
If someone takes notes they would be helpful to upload as well.
Awesome! If anyone has slides and are willing to share them, I would love to take a look. I suspect some folks @ HSPH might be interested in attending the talk part, if you let them know. (I am not in Boston anymore, sadly).
Hi Ilya,
I am giving this talk. If you want, I can send you the slides when they are ready. However, since you were the teaching assistant for the course where I learned this material, I very much doubt I will cover anything you aren’t already very familiar with…
Aww. I’d love to attend, but I’m on a different continent. Seconding the request to put the slides and notes online afterward.
Agreed.. I’d love to see this or read an article on this topic. I have the Pearl book, but it’s a bit dense for someone who doesn’t have an in-depth background in statistics.