Jakob Everding

Data Enthusiast | Machine Learning | Econometrics | Causal Inference
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[Jakob Everding @J_Everding]

@RicciMilstein @CHEyork @CastelliAdriana Congratulations! 💪

[Shreya Shankar @sh_reya]

Continuous Integration (CI) & testing for ML pipelines is hard and generally unsolved. I’ve been thinking about this for a while now — why it’s important, what it means, why current solutions are suboptimal, and what we can do about it. (1/10)

[Engineering @Engineering]

You may have heard about this year's Economics Nobel Prize winners - David Card, Josh Angrist (@metrics52) & Guido Imbens.

Their publicly available work has helped us solve tough problems @Twitter, and we're excited to celebrate by sharing how their findings have inspired us.

[Jonathan Mummolo @jonmummolo]

Our new working paper: tell the computer what you know (and don't know!) about a causal question w/ discrete data → automatically get most precise possible answer (bounds, or a point estimate). Joint w/ @guilhermejd1 @nsfinkelstein @dean_c_knox Shpitser.🧵https://t.co/9FY4HKCp32

[Sean J. Taylor @seanjtaylor]

Folks have asked me for references on the ML perspective on causal inference. Here are two good ones:

1. @matheusfacure's book "Causal Inference for the Brave and the True" (Part II) https://t.co/kdNpZLYXuH

2. @CasualBrady's lecture notes (Chapter 7) https://t.co/FFmN6NlYau

[Ziad Obermeyer @oziadias]

For the past 2 years, we’ve worked to diagnose and fix bias in dozens of algorithms.

@caseymross wrote a wonderful piece about it today: https://t.co/ujDB4bEOKQ

And our bias ‘playbook’ distills the process into 4 simple steps: https://t.co/TIhL6np9jd

🧵

[Hamburg Center for Health Economics @hche_uhh]

📰 Gutachten SVR Gesundheit:

Das neue Gutachten des Sachverständigenrates (SVR) Gesundheit, dem auch @JSchreyoegg angehört, ist veröffentlicht.

Dieses Mal steht es unter dem Thema #Digitalisierung für #Gesundheit 👇

https://t.co/3606ZUiOr1

[Women in Statistics and Data Science @WomenInStat]

See 🧵 below for a list of books on AI ethics, algorithm bias, automated decision making, etc. that I've enjoyed reading.

What's the most thought provoking book you've read lately?

[Women in Statistics and Data Science @WomenInStat]

Tweetorial on going from regression to estimating causal effects with machine learning.

I get a lot of questions from students regarding how to think about this *conceptually*, so this is a beginner-friendly #causaltwitter high-level overview with additional references.

[Daniela Witten @daniela_witten]

FOUR CARDINAL RULES OF STATISTICS 📈📊📉

A thread

1/🧵

[Judea Pearl @yudapearl]

ML will not be the same in 3-5 years, and ML folks who continue to follow the current data-centric paradigm will find themselves outdated, if not jobless. Take note.

[Judea Pearl @yudapearl]

The expression "causal effect" is a relic of a period when people (mostly stats.) thought there are "non-causal effects". I believe it was Don Rubin who insisted on the distinction. Today, when we understand that all "effects" are causal, "causal effect" is redundant poetry.

[Hamburg Center for Health Economics @hche_uhh]

🏛️We're hiring:

A professorship (W3) for Economics with a focus on empirical #healtheconomics at @unihh's Faculty of Business, Economics and Social Sciences. More details can be found here: https://t.co/3Xsq5jdeES.

Please share! #EconTwitter

[Johannes König @Joh_Koenig]

A big team at the SOEP has just published a new overview of the research potentials for economists in the SOEP. Link here: https://t.co/ftrrhLR5rg Topics include: quasi-experimental designs, experiments in the SOEP-IS, data-linkage, the wealth module, and much more.
#EconTwitter

[Hamburg Center for Health Economics @hche_uhh]

Today is #WorldNoTobaccoDay. So why not revisit the #healtheconomics paper by @J_Everding & @jmarcus_econ on the effect of unemployment on smoking behaviour of couples? https://t.co/QI3S7rlU4a.

[WirVsVirus @WirVsVirus]

📢📢📢 #WirvsVirus Hackathon der Bundesregierung

Nutz Deine Zeit sinnvoll. Reich Herausforderungen ein, mobilisier Deine Freunde, arbeitet digital an Lösungen, die uns näher zusammenbringen.

Sei dabei! Wir brauchen Deine Ideen & Fähigkeiten!
https://t.co/ecDtgUrNFg

[John Cawley @cawley_john]

Are you still looking for a job on the #EconJobMarket ? @Cornell has three open 3-year Research Assistant Professor positions in #DataScience (broadly construed). cc #EconTwitter @DataScienceCtrl Details: https://t.co/vxoPHJe1rn

[TensorFlow @TensorFlow]

The TensorFlow team has a new funding opportunity for university faculty interested in teaching ML, diversity, and inclusion.

Learn more here → https://t.co/iS4meRk9bp

[CHEPS @SDSUCHEPS]

. @J_Everding and @BigDataMetrics study the effect of medical marijuana laws on anti-depressant prescriptions. Their findings suggest that MMLs may improve mental health of populations that suffer from health conditions covered by state MMLs. #HCHE

[Claryn Kung @cclarynn]

So delighted to have @J_Everding visit us at @CHE_Monash the past few weeks—we enjoyed having you around! Thanks for the interesting & unique preso at @AusHealthEcon on the (upward!) intergenerational effects of education on health (across 11 countries!).