Discover and read the best of Twitter Threads about #econbookclub

Most recents (9)

1/ Alright folks, this will be the last round of #EconBookClub, discussing Guido Imbens' new paper on PO vs. DAGs (link: #EconTwitter #BookofWhy #Econometrics #Causality #CausalInference #MachineLearning #AI
2/ Today I will go through the final chapter 4.6 and the paper's conclusion. Maybe you will notice that I skipped over section 4.5. I had some thoughts on this chapter, but I feel that I didn't understand Guido's point well enough. So I prefer to leave it aside for the moment.
3/ In chapter 4.6, Guido discusses the returns to education example, which is a classic question in economics and which is also closely connected with the development of PO techniques in econometrics.
Read 19 tweets
2/ In this chapter, Guido responds to the main criticism that is brought forward towards the potential outcomes framework by proponents of DAGs – the choice of covariates or how to justify ignorability.
3/ We're all familiar with the ignorability / unconfoundedness assumption that underlies classical matching estimators, for example. The corresponding DAG is depcited in Figure 8a of the paper.
Read 17 tweets
1/ Let's continue with chapter 4.3 of Guido Imbens' new working paper on PO versus DAGs (link:…), this time with a discussion about simultaneity and cyclic models. #EconBookClub #BookofWhy #Econometrics #Causality #MachineLearning
2/ The chapter raises an important point. Many canonical models in economics are cyclic equilibrium models (in our language, we would call them "nonrecursive"). And DAGs are by definition acyclic, so they cannot really capture such models. But...
3/ The chapter follows a line of argument that is very similar to the last section, and which I don't agree with. It focusses on a special case (here a simple supply-and-demand equilibrium model) that lies outside the realm of DAGs, and which was solved by PO practitioners.
Read 17 tweets
1/ Alright, today in #EconBookClub I'd like to discuss chapter 4.2 of Guido Imbens' new working paper on the differences between the potential outcomes framework and directed acyclic graphs (link:…). #BookofWhy #Causality #Econometrics
2/ This chapter is about instrumental variable estimation, a topic to which Guido has contributed tremendously. Imbens and Angrist (1994) was definitely a big milestone in econometrics. I agree less with Guido's opinions on DAGs in this context though—as you might expect by now.
3/ Working with a graphical representation of a structural causal model (SCM) – i.e. the DAG – means that we explicitly refrain from making any distributional or functional form assumptions. That's a feature, not a bug!
Read 16 tweets
1/ Let's go for another round of #EconBookClub! By now, we've reached the most important chapter 4 of Guido Imbens' new paper in which he contrasts DAGs with the potential outcomes (PO) framework (link:…) #BookofWhy #Causality #Econometrics
2/ This chapter has six subsections and because my previous threads were already quite long, I decided to go thorugh them one-by-one in the following days.
3/ Section 4.1 is about the role of manipulability in causal inference. Something that has been discussed here a lot recently by the epidemiologists and also @yudapearl commented on it a while ago.
Read 15 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. #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/ Alright, time for round 3 of #EconBookClub! In this iteration I will cover chapters 2.4 to 2.9 of Guido Imbens' new working paper on PO vs. #DAGs (link:…). #BookofWhy #Causality #AI #MachineLearning
2/ The Twitter thread will probably be much shorter this time compared to the last ones, simply because Guido discusses a lot of terminology and technical details in these section, which I don't have much to comment on.
3/ But I'm extremely happy that Guido provides an introduction into the basics of DAGs and calculus to a broader audience in economics. Finally, I have a paper to cite when I use the terms "confounder" or "mediator", since this type of terminology,
Read 18 tweets
1/ It's again time for #EconBookClub! Let's continue our discussion of Guido Imbens' new paper on DAGs and PO. But first of all, let me thank all of you who were engaging with the first thread. It was really amazing to see all the reactions. Thanks!
2/ Alright, I managed to read sections 2.1 to 2.3 today, in which Guido reviews some parts of the #BookofWhy and provides an introduction into the fundamental ideas behind Directed Acyclic Graphs (DAGs) for a broader econometric audience.
3/ Guido is exactly right with his remark here, that a large focus of the causal inference literature in computer science is on identification. To understand this specific angle, you have to look at how CS folks approach the problem of causal inference.
Read 24 tweets
I'm very much looking forward to read Imben's new piece on PO and DAGs. Inspired by @EpiEllie's #EpiBookClub, I'd like to start something similar to delve deeper into the individual sections of the paper. So let's start #EconBookClub! #BookofWhy 1/
2/ After having read the introduction, it's very cool to see that Guido apparently had a change of mind with respect to the usefulness of DAGs. Compare this passage of the paper with his 2014 paper published in Statistical Science:…
3/ It's not exactly clear what Guido's personal stance is, but there's certainly a change in tone here. That's great, because it means that the paper could be a real starting point for having an objective discussion about how DAGs can add to our econometric toolbox.
Read 20 tweets

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