Optimal policy learning based on causal machine learning in R workshop

Join our workshop on Optimal policy learning based on causal machine learning in R, which is a part of our workshops for Ukraine series! 

Here’s some more info: 

Title: Optimal policy learning based on causal machine learning in R

Date: Thursday, May 16th, 18:00 – 20:00 CEST (Rome, Berlin, Paris timezone)

Speaker: Martin Huber earned his Ph.D. in Economics and Finance with a specialization in econometrics from the University of St. Gallen in 2010. Following this, he served as an Assistant Professor of Quantitative Methods in Economics at the same institution. He undertook a visiting appointment at Harvard University in 2011–2012 before joining the University of Fribourg as a Professor of Applied Econometrics in 2014. His research encompasses methodological and applied contributions across various fields, including causal analysis and policy evaluation, machine learning, statistics, econometrics, and empirical economics. Martin Huber’s work has been published in academic journals such as the Journal of the American Statistical Association, the Journal of the Royal Statistical Society B, the Journal of Econometrics, the Review of Economics and Statistics, the Journal of Business and Economic Statistics, and the Econometrics Journal, among others. He is also the author of the book “Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R.”

Description: Causal analysis aims to assess the causal effect of a treatment, such as a training program for jobseekers, on an outcome of interest, such as employment. This assessment requires ensuring comparability between groups receiving and not receiving the treatment in terms of outcome-relevant background characteristics (e.g., education or experience). Causal machine learning serves two primary purposes: (1) generating comparable groups in a data-driven manner by detecting and controlling for characteristics that significantly affect the treatment and outcome, and (2) assessing the heterogeneity of treatment effects across groups differing in observed characteristics. Closely related to effect heterogeneity analysis is optimal policy learning, which seeks to optimally target specific subgroups with treatment based on their observed characteristics to maximize treatment effectiveness. This workshop introduces optimal policy learning based on causal machine learning, facilitating (1) data-driven segmentation of a sample into subgroups and (2) optimal treatment assignment across subgroups to maximize effectiveness. The workshop also explores applications of this method using the statistical software “R” and its interface “R Studio.”

Minimal registration fee: 20 euro (or 20 USD or 800 UAH)

How can I register?

• Go to https://bit.ly/3wvwMA6 or https://bit.ly/4aD5LMC  or  https://bit.ly/3PFxtNA and donate at least 20 euro. Feel free to donate more if you can, all proceeds go directly to support Ukraine.

• Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)

• Fill in the registration form, attaching a screenshot of a donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after donation).

If you are not personally interested in attending, you can also contribute by sponsoring a participation of a student, who will then be able to participate for free. If you choose to sponsor a student, all proceeds will also go directly to organisations working in Ukraine. You can either sponsor a particular student or you can leave it up to us so that we can allocate the sponsored place to students who have signed up for the waiting list.

How can I sponsor a student?

• Go to https://bit.ly/3wvwMA6 or https://bit.ly/4aD5LMC  or https://bit.ly/3PFxtNA and donate at least 20 euro (or 17 GBP or 20 USD or 800 UAH). Feel free to donate more if you can, all proceeds go to support Ukraine!

• Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)

• Fill in the sponsorship form, attaching the screenshot of the donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after the donation). You can indicate whether you want to sponsor a particular student or we can allocate this spot ourselves to the students from the waiting list. You can also indicate whether you prefer us to prioritize students from developing countries when assigning place(s) that you sponsored.

If you are a university student and cannot afford the registration fee, you can also sign up for the waiting list here. (Note that you are not guaranteed to participate by signing up for the waiting list).

You can also find more information about this workshop series,  a schedule of our future workshops as well as a list of our past workshops which you can get the recordings & materials here.

Looking forward to seeing you during the workshop!