Atelier sur l'évaluation quantitative de l'impact/Études de cas II
Atelier sur l'EQI - Session 4 - Études de cas II
Ordre du jour | 28 mars | 9h00 à 12h00 HE
| Étude de cas 1 | Propensity score matching (PSM) and entropy balancing for impact assessment of business innovation and growth support (BIGS) on small and medium sized enterprises | Ibrahim Bousmah | |
| Étude de cas 2 | Matching difference-in-difference (MDID) for evaluation of AgriInnovation Stream C and AgriInnovate commercialization programs | Mohammad (Moh) Torshizi | |
| Étude de cas 3 | Modified Causal Forest (MCF) method to estimate incremental program impacts for different GBA Plus intersecting identity factors | Christiane Arsenault and Yu-Hsien Liu |
Meet the Presenters!
Ibrahim Bousmah
Ibrahim Bousmah is an economist with the Treasury Board of Canada Secretariat. He received his Ph.D. in Economics from the University of Ottawa. He is particularly interested in research related to applied econometrics. His other research interests include innovation, entrepreneurship, firm performances, wages, and other labor market attributes.
Mohammad (Moh) Torshizi
Moh received his PhD in Agricultural Economics at the University of Saskatchewan in 2015. He also hold B.Sc. and M.Sc. degrees in Agricultural Economics. Before joining the Research and Analysis Directorate at AAFC in 2020, he was an assistant professor of Agribusiness at the University of Alberta. Moh’s research interests are program impact assessment; economics of innovation, competition, and interactions of the two in agri-food systems; and grain marketing, handling, and transportation. He has publications in Ecological Economics, American Journal of Agricultural Economics, Canadian Journal of Agricultural Economics, Australian Journal of Agricultural and Resource Economics, Journal of Agricultural and Food Industrial Organization, Antitrust Bulletin, and the Journal of Transportation Research Forum.
Christiane Arsenault
Christiane Arsenault leads a Quantitative Methodology team at ESDC's Evaluation Directorate. She and her team develop advanced analyses to assess the effectiveness of ESDC programs. Before this, she helped other departments to benefit from innovative data analytics and transformation to make business processes more effective, efficient, and productive. She is passionate about improving our work and fostering cross-functional collaboration.
Yu-Hsien Liu
Yu-Hsien Liu is a Senior Data Analyst in the ESDC Evaluation Directorate. Bringing a hands-on approach to data science, she implemented a machine learning method to uncover new insights on program effectiveness from a Gender-Based Analysis Plus perspective. Yu-Hsien excels in leveraging R, Python, and visualization tools to derive meaningful insights, ensuring a comprehensive understanding of complex datasets.
