Topics and Objectives

Module Schedule

During this three-week module, we will cover the following potential lecture materials as time permits. The first two weeks will focus mainly on causal inference, conditional average treatment effects, and outcome weighted learning. Homework 2 covers both Modules 2 and 3 and is due November 5. The presentation schedule for this module will be announced later.

The IV lecture is a separate identification route when unobserved confounding is a concern. The CATE and outcome weighted learning lectures then return to the conditional unconfoundedness assumption.

Homework

Presentation Session

General logistics and scoring are described on the Present and Challenge page.

Teams assigned to present in this module should select an applied paper on causal inference from the list below. Each team may use up to 12 minutes for its presentation and up to 3-5 minutes for Q&A. The specific time allocation may be shorter depending on the number of teams presenting in the session.

For this module, focus on the reasoning behind the choice of causal inference method. Numerical experiments are not required.

The basic requirements are:

  1. Introduce the scientific question and data, and explain why the data collection and structure make causal inference challenging.
  2. Introduce the method and explain why it is appropriate for this problem and data, compared with plausible alternatives.
  3. Assess whether the conclusions are convincing, and why, considering the method’s assumptions and limitations in this setting.

Teams may also propose a paper of their own choice. The paper should:

Here are some candidate papers: