Treatment Effect Estimation and Heterogeneous Effects

Machine Learning › AI Causal Machine Learning Research

Treatment Effect Estimation and Heterogeneous Effects

Interns will develop and implement methods for estimating causal treatment effects including propensity score matching, doubly robust estimation, and machine learning-based approaches like causal forests. They will focus on measuring conditional average treatment effects (CATE) and understanding how treatment effects vary across different population subgroups.

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