Thread by @WomenInStat

Today, I would like to share some resources on causal inference. - a thread ⬇️

I came to this topic, while working with clinicians who use IPW and matching on a daily basis (they are not familiar with double robust approaches). I don’t know for you, but I am so admirative of them as they combine their work with patients with research to advance knowledge

I recommend @Susan_Athey's videos (such as aeaweb.org/webcasts/2018/…), an example of pedagogy - they also make available the analysis notebooks, gsbdbi.github.io/ml_tutorial/in…. For a longer and more technical course see, Stefan’s Wager web.stanford.edu/~swager/stats3…

@Susan_Athey I also recommend Jonas Peter’s lecture stat.mit.edu/news/four-lect…, covering more the Structural Causal Model’s point of view and highlighting recent works on invariant predictions. For a longer course see @heinzedeml with stat.ethz.ch/lectures/ss20/…

@Susan_Athey @heinzedeml I can also share the google doc, getting started with causal inference, with many pointers tinyurl.com/du69bt3m done mainly by @imkemay, for our causal inference and missing data group (CIMD) at @Inria. I share this to anyone who tries to navigate in this productive field.

@Susan_Athey @heinzedeml @imkemay @Inria Note that this is a field where women are well represented and where there are many inspiring figures! see for instance the speakers at the Online Causal Inference Seminar.