At the Competence-Centre on Microeconomic Evaluation (CC-ME) we advise and support EU policy making through ex-post causal evaluation and data-driven microeconomic analysis. The CC-ME hosts a Microeconometric Seminar Series to promote discussions with external researchers from academia and other institutions. Our Seminar Series is intended to disseminate advanced research methodologies and topics in the field of microeconomic evaluation. To further disseminate the benefits of our Series across the JRC, we post a summary of the presented papers together with the presenters' views and opinions on their research and the future of the field of Applied Economics. |
Last week we had the pleasure of having Professor Cerqua from UniRoma present his work: Causal inference and policy evaluation without a control group.
In his work, Professor Cerqua proposes the Machine Learning Control Method (MLCM), a new approach for causal panel analysis based on flexible counterfactual forecasting with machine learning. The MLCM estimates policy-relevant causal parameters without relying on untreated units.
He illustrates the practical relevance of the MLCM with simulation evidence, a replication study, and an empirical application on the impact of the COVID-19 crisis on educational inequality.
To learn more about Professor Cerqua's work and opinions about the future of the field of Applied Economics we asked him to briefly answer a series of questions. You can find his answers to each of our questions below.
Q: What attracted you to research the topics in your paper? A: I have been working on the estimation of causal effects for over ten years. At a certain point, I noticed that some research questions could not be addressed with the traditional evaluation tools available in causal inference, especially when all units are treated simultaneously. This is why I began developing a method that provides a reliable estimation of causal effects in challenging evaluation settings, specifically designed for the relatively common scenario where only a few pre-treatment time periods are available in a panel dataset. |
Q: Where is the research area where your paper fits moving? A: Over the last few years, other scholars have begun to develop estimators for similar evaluation settings, for instance, by adapting estimators traditionally used in time-series literature. I believe that extending the settings in which causal effects can be estimated represents a very promising direction for future research in causal inference. |
Q: What, in your opinion, will the next breakthrough in Applied Economics be? A: Of course, further integration of machine learning and deep learning with traditional estimators used in causal inference is essential. Over the last 10 years, significant progress has been made in this area, but I believe it remains substantially under-researched. |
The CC-ME team would like to congratulate Professor Augusto Cerqua for his insightful research and thank her for presenting it in our Seminar Series.
For more information on the upcoming presentations and how to participate in our Seminar Series please visit our dedicated website.
| Originally Published | 19 Feb 2025 |
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