Textbooks

The followings are useful textbooks for the course.

• (C) Judea Pearl (2009) Causality
• (CIWI) Miguel A. Hernán, James M. Robins (2020) Causal Inference: What If
• (ECI) Jonas Peters, Dominik Janzing, Bernhard Schlkopf (2017) Elements of Causal Inference: Foundations and Learning Algorithms
• (ICI) Brady Neal (2020) Introduction to Causal Inference
• (PGM) Daphne Koller and Nir Friedman (2009) Probabilistic Graphical Models - Principles and Techniques

Tentative Schedule

Date	Topic	Readings
09/12/2022	Introduction and Motivation We start the course by looking at applications where statistical association cannot fully capture the underlying data-generating processes. We then introduce randomized controlled trials and observational studies to understand the difference between association and causation. We will use the potential outcome framework to quantify the causal effects in a simple binary case.	CIWI 1, 2.1-2.2, 3.1-3.5 ICI 1 & 2 PGM 3
09/19/2022	Causal Models Conditional distributions are not enough to model and estimate causal effects. In this lecture, we'll see how the notion of interventions is a general way to quantify causal effects. In particular, we'll use causal graphs and structural causal models (SCMs) to formally define interventional distributions.	ECI 2, 6.1-6.3 ICI 3, 4.1-4.3 C 1.2-1.4 Inverse CDF: link1, link2
09/26/2022	Identification In order to estimate causal effects from data, we first need to convert them to a function of data (observational distribution). This process is known as identification. We use causal graphs to study sufficient conditions for identification, such as Back-door and Front-door criteria. We then introduce do-calculus as a complete non-parametric identification algorithm.	C 3.1-3.4 ECI 6.4, 7.1-7.2 ICI 6.1-6.2
10/03/2022	Estimation Once we have identified a causal query, we still need to build efficient estimators to estimate it from finite data. This lecture focuses on some well-known estimators in the literature, including parametric g-formula, propensity scores, inverse propensity weighting, and matching.	ICI 7.1-7.6 TAR-Net Propensity Score
10/10/2022	No Class - Thanksgiving	-
10/17/2022	Instrumental Variables + Student Paper Presentation	ICI 9.1-9.4 ECI 9.3, link
10/24/2022	Student Paper Presentation	-
10/31/2022	Student Paper Presentation	-
11/07/2022	No Class - Reading Week	-
11/14/2022	Invariant Learning + Student Paper Presentation	ICP paper
11/21/2022	Double Machine Learning	DML paper DML application
11/28/2022	Student Project Presentation	-
12/05/2022	Student Project Presentation	-