from Adam Kelleher:
The math and algorithm reading group (http://www.meetup.com/Math-and-Algorithm-Reading-Group/) is based in NYC, and was founded when I moved here three years ago. It’s a very casual group that grew out of a reading group I was in during graduate school. Some friends who were math graduate students were interested in learning more about general relativity, and I (a physicist) was interested in learning more math. Together, we read about differential geometry, with the goal of bringing our knowledge together. We reasoned that we could learn more as a group, by pooling our different perspectives and experience, than we could individually. That’s the core motivation of our reading group: not only are we there to help resolve each other get through the material if anyone gets stuck, but we’re also there to add what else we know (in the format of a group discussion) to the content of the material.
We’re currently reading Causality cover to cover. We’ve paused to implement some of the algorithms, and plan on pausing again soon for a review session. We intend to do a “hacking session”, to try our hands at causal inference and analysis on some open data sets.
Inspired by reading Causality, and realizing that the best open implementations of causal inference were packaged in the (old, relatively inaccessible) Tetrad package, I’ve started a modern implementation of some tools for causal inference and analysis in the causality package in Python. It’s on pypi (pip install causality, or check the tutorial on http://www.github.com/akelleh/causality), but it’s still a work in progress. The IC* algorithm is implemented, along with a small suite of conditional independence tests. I’m adding some classic methods for causal inference and causal effects estimation, aimed at making the package more general-purpose. I invite new contributions to help build out the package. Just open an issue, and label it an “enhancement” to kick of the discussion!
Finally, to make all of the work more accessible to people without more advanced math background, I’ve been writing a series of blog posts aimed at introducing anyone with an intermediate background in probability and statistics to the material in Causality! It’s aimed especially at practitioners, like data scientists. The hope is that more people, managers included (the intended audience for the first 3 posts), will understand the issues that come up when you’re not thinking causally. I’d especially recommend the article about understanding bias https://medium.com/@akelleh/understanding-bias-a-pre-requisite-for-trustworthy-results-ee590b75b1be#.qw7n8qx8d, but the whole series (still in progress) is indexed here: https://medium.com/@akelleh/causal-data-science-721ed63a4027#.v7bqse9jh