Discover and read the best of Twitter Threads about #econometrics

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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/ Alright folks, this will be the last round of #EconBookClub, discussing Guido Imbens' new paper on PO vs. DAGs (link: arxiv.org/abs/1907.07271). #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: arxiv.org/pdf/1907.07271…), 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: arxiv.org/pdf/1907.07271…). #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: arxiv.org/pdf/1907.07271…) #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. 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
Happy 4th of July!!
One area of intergration of ML and econometrics is providing inference after variable selection (Post selection Inference) #rstats #econtwitter #Rladies #ML #econometrics 1/n
Most popular technique in economics is the 'Double LASSO' which provides inference on the treatment effect after variable selection using LASSO. Check out the R package 'hdm'. cran.r-project.org/web/packages/h…
#rstats #econtwitter 2/n
We are often interested in conducting inference on other selected covariates (controls) as well. Check out R package 'selectiveInference' which conducts inference on multiple covariates. cran.r-project.org/web/packages/s…
#rstats #econtwitter 3/n
Read 5 tweets
#Bigdata vs Machine Learning vs Artificial Intelligence
By Irene Aldridge
☝️author High-Frequency Trading: A Practical Guide to #Algorithmic Strategies& #Trading Systems
☝️co-author Real-Time Risk: What Investors Should Know About #Fintech,#highfrequencytrading and FlashCrashes
1. ☝️🧐➿🔢 In traditional #statistics or #econometrics, researchers make assumptions about #data distributions ahead of the analysis
2. 🥇💪#machinelearning = the 1st discipline to apply #efficiency to #problemsolving brought by #computers & their enhanced computational power
3. 🤗➿🔢#ML scientists try to reduce #assumptions about the data as much as possible&let the data (&computers) decide what fits best.
4. ♾➡️🔤🌐#Datascience identifies core characteristics of the data, summarized by what has been known as #characteristic values (#eigenvalues)
Read 6 tweets

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