Discover and read the best of Twitter Threads about #DAG

Most recents (5)

Finally, @eliasbareinboim and I are able to share a working paper that we've been working on for quite a while now:

"Causal Inference and Data-Fusion in Econometrics"
causalai.net/r51.pdf

🚨 #DAG #CausalInference #Econometrics #DataScience #MachineLearning #AI 1/
2/ The paper provides a comprehensive overview of recent developments in the causal AI field and discusses how DAGs can contribute to econometrics. One of our main motivations for starting this project was that the handful of papers that were written on DAGs by economists
3/ seem to be based on a state of the literature in around 2000, when "Causality" by @yudapearl was first published. But DAGs have a lot more to offer than just backdoor and frontdoor adjustment. And the size of the literature has literally exploded in the last 8 years or so.
Read 11 tweets
My new course "Causal Data Science with Directed Acyclic Graphs" has finally been published at @udemy. And I'm super excited to share it with you! udemy.com/course/causal-… #CausalAI #Causality #MachineLearning #DataScience #Econometrics #DAG #MOOC
In the past, I've already given similar workshops on causal inference. And people often asked me afterwards for recommendations of good online teaching resources. Unfortunately, there's really not that much out there. So, I decided to do something about it.
Please have a look and let me know what you think! For me personally, this is my first experience with MOOC education. I'm pretty happy with the result. But decide for yourself (there's a free intro video on the course landing page, btw).
Read 10 tweets
1/ It's #EconBookClub time again. Today, I will focus on chapter 3 of Guido Imbens' new paper, in which he compares the potential outcome framework with graphical approaches to #causation. arxiv.org/abs/1907.07271 #BookofWhy #DAG #Causality #AI #Econometrics
2/ After having introduced his readers to the fundamentals of DAGs, Guido now constrasts them with the potential outcomes framework, to which he contributed massively in his work within econometrics.
3/ Again, this chapter of the paper is mostly definitional, so I only have a couple minor comments. But I would like to pick up some of the threads that the paperleaves open in certain places, in my view.
Read 23 tweets
1) @realDonaldTrump @WhiteHouse @POTUS
#QAnon No. 3195...
Link 2 #CSpan ==> Flowers v. Mississippi Oral Argument 20th March 2019.
This case is concerned w/racial bis=as in jury selection.
c-span.org/video/?458080-…
What is #RBG's current state of health. It's clearly not well
2) #DSMedia could cover her movements to & from hearings, like the one in this link, but it wouldn't fit their operational parameters. They don't want it out that her health is failing, so you don't see images of her coming & going from home or her vehicle. They'd be easy shots.
Read 31 tweets
Are you still shaking off the holiday? I know I am!

How about a #cartooncausalinference #tweetorial about casual graphs to ease us into the new year?

#epitwitter #DAGsfordocs #FOAMed #MedEd #statstwitter #econtwitter
The most common type of causal graph (at least on #epitwitter) is the directed acyclic graph, or #DAG.

DAGs have two main components: variables (also called nodes), and arrows (also called edges).

In the DAG below, there are 3 variables: sleeping, Santa, and presents.
The variables are ordered based on time — you have to go to sleep before Santa can come to your house & then he’ll leave presents!

Causation and time both flow in the direction of the arrows.
Read 24 tweets

Related hashtags

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just three indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3.00/month or $30.00/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!