Korea Summer Session on Causal Inference 2021

The Korea Summer Session on Causal Inference aims at introducing research methodologies and data analytical approaches in pursuit of causal inference. Across multiple disciplines, causal inference has been achieved largely building on two frameworks: (i) Potential Outcomes Framework (design-based approach) and (ii) Structural Causal Model (structure-based approach). In addition, machine learning has been rapidly employed for causal inference in ways to improve the quality of causal analysis, estimate causal effects on top of underlying causal structures, and/or discover causal relationships from large quantities of data.

Against this backdrop, the two modules of the summer session are geared toward two learning goals (see Lecture Materials for details):

[Module 1] To understand research designs for causal inference

[Module 2] To understand how machine learning complements and innovates causal inference