MATH161 CM - Causal Inference and Experimental Design

Causal inference is the central task of scientific inquiry, allowing researchers to establish whether a novel intervention is beneficial. This course surveys fundamental ideas and methods of causal inference and experimental design. Key topics will include the potential outcomes framework; randomization-based inference; and methods for establishing causality in observational studies such as propensity scores, matching, and sensitivity analysis. We will also discuss how to improve experiments in the design phase, highlighting canonical designs such as Neyman Allocation and blocked and factorial experiments. Emphasis will be on theory, but students will apply these methods to real datasets in R.

Prerequisites: MATH 151 CM and MATH 152 CM

Offered: Every other year

Credit: 1

Course Number: MATH161 CM