Matheus Facure Alves
@MatheusFacure
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Economist | Causal Inference | Data Scientist at @nubank | Author of Causal Inference for the Brave and True | 🇧🇷
Joined April 2017
After a year filled with intense writing, I can't believe Causal Inference in Python is finally going to print!! Not only that, but I must say the book looks absolutely beautiful! You can now preorder it on Amazon: https://t.co/N4bFVZrfsw
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Huge thanks to @MatheusFacure for teaching me the majority of the applied econometrics I know through his excellent free book... https://t.co/mTLnBp7iV8
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💳💳💳💳 I recently finished a revision of my paper on how credit card lenders set credit limits: 💳💳💳💳
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And two more to show what a baseline (stupid model that just outputs random numbers) looks like:
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Two gifs for explaining the cumulative gain (quini) and effect curves when the treatment is continuous.
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Works like a charm. We had loads of fun building a farm and now we're having a blast not knowing wtf we are doing with a bunch o magic spells.
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A math department at a major university in the US is now offering a semester long course on “game theory”. Yes, you read that right. Games. The things tiny children play. The dumbing down of America continues…
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Your periodic reminder that economists come in different shapes and sizes. There are academic economists. There are government & central bank economists. There are think tank economists. There are private-sector economists. Each come with useful perspectives & tools.
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The only 3 “self-help” books you will ever need
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🥲😀
@MatheusFacure I am pleased to let you know that the Korean edition of <Causal Inference in Python> has been published. Since its release, it has received warm support from Korean readers.
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🧵New @pymc_devs tutorial🧵 on Bayesian Non-Parametric Causal Inference https://t.co/gc5DF0ejoB Bayesian non-parametrics lend themselves to analysis of causal questions. From BART to estimate propensity scores we move to double ML methods and non-parametric causal mediation!
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I'll join Alex today to talk about causal inf. and econometrics in fintech.
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Enjoying @MatheusFacure's book: Causal Inference for the Brave and True. For me, a nice blend of refreshers and new perspectives on causality. Learning a LOT. Great for a cozy evening read. And those memes? 💯
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Had the opportunity to speak at USC's Information Sciences Institute last week on Visualizing and Analyzing Event Sequence Data. I dove deep into the science behind @MotifAnalytics. It captures my current and best thinking on approaching analytics from a causal perspective.
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Less than two weeks!
🚨Atenção!! Ainda há tempo para fazer a sua inscrição para participar do minicurso do professor @jmwooldridge (Michigan State University)
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I know an awful lot about causal inference, but this is the clearest explanation of the “ladder of causation” I’ve ever read. I think I *finally* get the point @yudapearl was trying to make with this. Still not sure I think it’s quite as useful as he does, tho 😅
The Ladder of Causation This is a fundamental idea that everybody who cares about data needs to know about. 1. The Ladder of Causation has three levels. 1. Association. This involves the prediction of outcomes as a passive observer of a system. 2. Intervention. This involves
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