Utilizing meta-learning to recommend causal effect estimators
Organization
Data Systems
Abstract
Estimation of causal effects using machine learning methods has become an active research field. Machine learning estimators as such are, however, primarily designed for prediction problems and thus cannot be used directly for causal inference. Therefore, new approaches for the estimation of causal parameters using machine learning emerged, such as meta-learners, specialized variants of tree-based models, and dedicated neural network architectures for estimating causal effects. The goal of this thesis is to utilize meta-learning techniques to recommend estimators for the estimation of heterogeneous treatment effects. More specifically, build a meta-model that learns the relationship between the characteristics of the task at hand and predicts the best meta-learner. This work will focus on the estimators implemented in the EconML library https://econml.azurewebsites.net.
Graduation Theses defence year
2023-2024
Supervisor
Radwa El Shawi
Spoken language (s)
English
Requirements for candidates
Level
Masters
Keywords
Application of contact
Name
Radwa El Shawi
Phone
E-mail