Causal analysis is rapidly gaining popularity, but why? Machine learning methods might help us predict what’s going to happen with great accuracy, but what’s the value of that if it doesn’t tell us what to do to achieve a desirable outcome? Without a causal understanding of the world, it’s often impossible to identify which actions lead to a desired outcome.

Causal analysis is often embedded in a frequentist framework, which comes with some well-documented baggage. In this talk, Thomas will present how we can super-charge PyMC for Bayesian Causal Analysis by using a powerful new feature: the do operator.

We are holding office hours to help YOU apply to PyMCon. Whether you have a finished talk and are looking for feedback, or just have an idea, we’ll help you get moving.

You can participate in two ways:

• - For asynchronous discussions, join our discourse thread and share your amazing ideas: https://discourse.pymc.io/t/12689
• - For synchronous discussions, join our Meetup event: https://www.meetup.com/pymc-online-meetup/events/295283022/

Our supportive community is excited to offer feedback, guidance, and collaboration to help you bring your ideas to life. Together, let’s take your contributions to the next level and make a positive impact.

Inspired by the asset-pricing literature, the Soccer-Factor-Model (SFM) is an attempt to determine a soccer player’s alpha, i.e. his/her inherent skill. In this application, skill is defined as a player’s probability to score a goal after accounting for factors that are actually to be attributed to his team’s out- or under-performance vis-à-vis the opponent.

In that way, the SFM answers the question, of whether a player’s observed goals are an over- or understatement of his true skill.