{"id":4989,"date":"2023-01-19T10:26:27","date_gmt":"2023-01-19T09:26:27","guid":{"rendered":"https:\/\/datascience.unifi.it\/?post_type=tribe_events&p=4989"},"modified":"2023-01-27T13:35:33","modified_gmt":"2023-01-27T12:35:33","slug":"seminar-of-the-d2-seminar-series-florence-center-for-data-science-7","status":"publish","type":"tribe_events","link":"https:\/\/datascience.unifi.it\/index.php\/event\/seminar-of-the-d2-seminar-series-florence-center-for-data-science-7\/","title":{"rendered":"Seminar of the \u201cD2 Seminar Series\u201d \u2013 Florence Center for Data Science"},"content":{"rendered":"

Welcome to another seminar of the D2 Seminar Series of the Florence Center for Data Science!<\/p>\n

We’re happy to host\u00a0Augusto Cerqua\u00a0<\/strong>and\u00a0Marco Letta\u00a0<\/strong>from the Department of Social Sciences and Economics of Sapienza University of Rome.<\/p>\n

The\u00a0Seminar<\/span>\u00a0will be held both on-site and online\u00a0Friday 3rd of March 2023<\/b>, from 2.30-4 pm<\/b>.<\/div>\n
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The seminar will be held in Aula 205 (ex 32) (DISIA \u2013 Viale Morgagni 59).<\/div>\n
The Seminar will be available also online. Please register here to participate online:<\/div>\n<\/div>\n
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https:\/\/us02web.zoom.us\/webinar\/register\/WN_GprXsGF7Ti6sQ8uyNfqKpQ<\/a><\/span><\/div>\n<\/div>\n
Speakers:<\/strong> Augusto Cerqua and Marco Letta\r\nTitle<\/strong>: \"Losing control (group)? The Machine Learning Control Method for counterfactual forecasting\"\r\nAbstract<\/strong>: The standard way of estimating treatment effects relies on the availability of a similar group of untreated units. Without it, the most widespread counterfactual methodologies cannot be applied. We tackle this limitation by presenting the Machine Learning Control Method (MLCM), a new causal inference technique for aggregate data based on counterfactual forecasting via machine learning. The MLCM is suitable for the estimation of individual, average, and conditional average treatment effects in evaluation settings with short panels and no controls. The method is formalized within the Rubin\u2019s Potential Outcomes Model and comes with a full set of diagnostic, performance, and placebo tests. We illustrate our methodology with an empirical application on the short-run impacts of the COVID-19 crisis on income inequality in Italy, which reveals a striking heterogeneity in the inequality effects of the pandemic across the Italian local labor markets.<\/pre>\n","protected":false},"excerpt":{"rendered":"
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