Alexander Quispe Rojas
@Alex_QR_CG
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Trained at Guadalupe+PUCP+LMU Munich+Harvard+MIT https://t.co/wirqsgUJXS Dive into Causal Machine Learning: https://t.co/I2Svox530d
Boston
Joined January 2019
🚀 I share my interview with @kevinxu from @github, where he delved into the paper I co-authored with @RodrigoGrijalb1.: "Impact of the Availability of ChatGPT on Software Development: A Synthetic Difference in Differences Estimation using GitHub Data".
github.blog
An interview with economic researchers who are applying causal inference techniques to analyze the effect of generative AI tools on software development activity.
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Lectura recontra obligatoria! #JavierHerrera.
✍ Lee la columna de Javier Herrera: "La verdad de las mentiras: El indicador de pobreza multidimensional".
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RT @erikbryn: MIT's decision to drop the mandatory DEI statement shows leadership. Ditto with requiring standardized tests again. There sh….
unherd.com
In what’s likely to be a watershed moment, the Massachusetts Institute of Technology has ended the use of diversity statements for faculty hiring, making it the first elite private university to...
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RT @ashdgandhi: In case it's helpful for current econ PhD students: I've literally never met an economist that felt their consumption in gr….
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Me in DC but without the $5 million, for now.
Today, I visited the home of a founder who raised $5 million for his AI startup. This is his setup 👇
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#EconTwitter for those interested in sensitivity analysis tools, @RodrigoGrijalb1 and I have created the Julia version of Sensemakr! Thanks to @analisereal for the comments and feedback. @JuliaForDataSci @julialang_es @julia #JuliaLang @JuliaLanguage .
github.com
Julia implementation of the original R sensemakr package: https://github.com/carloscinelli/sensemakr - d2cml-ai/Sensemakr.jl
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R, Python, Julia or Matlab? Maybe the first three?. "So within the same source file, one could use Python for data handling, R for plotting and Julia for fast computations. We have done so in several applications, and it works quite well.".
cepr.org
Economic researchers can choose between four general purpose programming languages – Julia, MATLAB, Python, and R. This column evaluates the four, comparing and contrasting the various language...
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I want to thank @Susan_Athey, @VC31415, and @smolix for letting me attend their lectures where I learned a lot!.
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#EconTwitter!willing to learn recent methods in Causal ML? Visit Tutorials are in R #python #JuliaLang.14.38-MIT..MGTECON634-Standford.
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The StoreyTelling with DATA PROCESS!!!!!🤓🤓🤓.
Want to transition from making graphs to telling data stories? Here's a 6-step process. (Greater detail in my books:
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For everyone interested in Structural Causal Models!.
Structural Causal Models are now available in #pywhy Thanks to Peter Goetz, Dominik Janzing, @kailashbuki and the rest of the @awscloud causality research team.
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A toda la gente de Perú, no dejen pasar esta oportunidad! Sorbetodo si planean seguir estudios en el extranjero! 🤓🤓🤓.
¿Eres un joven talentoso que quiere hacer un posgrado en #economía, pero no tienes recursos? ¡Esta es tu oportunidad! La Maestría en Economía de la @UdelPacifico abre su convocatoria para sus becas. Ayúdenme por favor🙏con un RT para que más jóvenes talentosos se enteren.
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Description of my life😅😅😅.
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current status.
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If you want to know more about the economic history of Peru you should *read* this book by Professor Carlos Contreras!.
¡Más tiempo que nunca!.😀.Desde mañana @IEPeruanos pondrá a la venta este súper libro con una síntesis de los últimos 500 años de economía peruana a cargo de quien quizá es el mayor especilialista en el tema. No se lo pierdan. Es un libro que apunta a clásico. 1/2
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How important is to understand the intersection of Machine Learning and Causal Inference! 🤓.
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.
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Necesario leerlo para estar informados!.
son programas sociales orientados a aliviar la pobreza (Juntos, P65). Por supuesto, estos son solo algunos casos, hay muchos más y la realidad siempre termina siendo más compleja, pero es un buen punto de partida. Más aún, este ejercicio resalta la necesidad de incorporar [11/n].
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Mis alumnos del curso de Inferencia Causal y Machine Learning en la PUCP 😅😅😅😅.
Statistics classes: R^2 is a measure of variance explained and is between 0 and 1 and cannot be negative. sklearn:
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