2022 CES Conference / Congrès 2022 de la SCÉ

Our study will show how the Evaluation directorate at Employment and Social Development Canada uses rich administrative data and Modified Causal Forests, a causal machine learning estimator, to inform policy development through impact evaluations. We will illustrate our implementation of the innovative Modified Causal Forests algorithm to estimate individualized treatment effects, thereby learning what works for whom. This endeavour is fully aligned with the Government of Canada's commitment to implement a Gender-Based Analysis+ lens in evaluation work, ensuring that differential impacts on people of various sociodemographic backgrounds are considered in policy and program development.