Welcome

Cornell STSCI / INFO / ILRST 3900. Causal Inference. Fall 2025.

Welcome! Together, we will learn to reason about and assess the plausibility of causal claims by combining data with assumptions.

Taught by Christina Lee Yu, Y. Samuel Wang, Filippo Fiocchi, and Shira Mingelgrin. Read about us here!

Learning objectives

As a result of participating in this course, students will be able to

• define counterfactuals as the outcomes of hypothetical interventions
• identify counterfactuals by causal assumptions presented in graphs
• estimate counterfactual outcomes by pairing those assumptions with statistical evidence

Is this course for me?

The course is designed for upper-division undergraduate students. We will assume familiarity with an introductory statistics course at the level of STSCI 2110, PAM 2100, PSYCH 2500, SOC 3010, ECON 3110, or similar courses. We will also assume that students are familiar with the statistical computing language R.

Not a Cornell student? You are welcome to follow along on this site.

Readings

Especially in the beginning, this course draws heavily on

Hernán, M.A., and J.M. Robins. 2020. Causal Inference: What If? Boca Raton: Chapman & Hall / CRC.

We are grateful to the authors for this excellent text.

Organization of the site

Each course module in the left panel will span several lectures. Within each module, the right panel will help you navigate. We will build this site over the course of the semester, uploading lecture slides as we go. Who we are tells you a bit about the teaching team.

Previous iterations of the course

Much of the material for this course will draw directly from previous iterations of the course which were also developed by Ian Lundberg and Mayleen Cortez-Rodriguez.

To access the course website from Fall 2023 click here.