ORIE 6745: Causality and Learning for Intelligent Decision Making

Course info

Time: Wednesdays 11:35–12:50 and 13:10–14:25
Location: Upson Hall 202 (Ithaca) and Bloomberg Center 091 (New York)
Note: Physical and virtual lectures will alternate periodically between Ithaca and New York. The first two lectures will be physically in Ithaca and virtually in New York.
Registration options: Both grade and non-grade allowed. Auditors allowed.
Prerequisites: familiarity with basic statistics, probability, and calculus, or permission of instructor.
Reading material: The course will follow instructor’s lecture notes and some external reading materials such as book chapters and research papers, which will be distributed electronically.
Grade breakdown: participation and lecture scribing (25%), homework (25%), final project (50%).

Slack channel


Nathan Kallus
Office hours by appointment

Course overview

The course introduces students to fundamental principles in causality and machine learning for decision making. Some of the most impactful applications of machine learning, whether in online marketing and commerce, personalized medicine, or data-driven policymaking, are not just about prediction but are rather about taking the right action directed at the right target at the right time. Actions and decisions, unlike predictions, have consequences and so, in seeking to take the right action, one must seek to understand the causal effects of any action or action policy, whether through active experimentation or analysis of observational data. In this course, we will study the interaction of causality and machine learning for the purpose of making decisions. In the case of known causal effects, we will briefly review the theory of generalization as it applies to designing action policies and systems. We will then study causal inference and estimation of unknown causal effects using both classical methods and modern machine learning and optimization methods, considering a variety of settings including controlled experiments (A/B testing), regression discontinuity, instrumental variables, and general observational studies. We will then study the direct design of action policies and systems when causal effects are not known, looking closely both at the online (contextual bandit) and offline (off-policy learning) cases. Finally, we will study ancillary consequences of intelligent systems’ actions, such as algorithmic fairness. The course will culminate in a final project.

Selection of topics covered

Lectures (updated as we go)

Title (note lecture may spill into one another) Scribe Notes Slides/Board Additional reading Assignments
1 Introduction Lec1.pdf
2 Elements of causality and decision making Lec2.pdf Lec 2 white board not recordedImbens & Rubin ’15 Chs. 1–3
3–4 Generalization bounds Lecs3-4.pdf WhiteboardLecs3-4.pdf Pollard's Iowa Notes,
Mohri et al. ’12 Chs. 1–2,
Bartlett & Mendelson ’02
5–6 Controlled Experiments Lec5.pdf,
WhiteboardLecs5-6.pdf Imbens & Rubin ’15 Chs. 4–7,
Rosenbaum ’02 Ch. 2,
Big O in probability
7–8 Inference in Controlled Experiments Lecs7-8.pdf WhiteboardLecs7-8.pdf Good ’05, Lin ’13, Ding ’17
9–11 Covariate Balancing Designs Lecs9-10.pdf WhiteboardLecs9-10.pdf,
Lec 11 white board not recorded
Kallus ’17,Greevy et al. ’04,
Imbens & Rubin ’15 Chs. 9–10
12–13 Noncompliance and IVs Being scribed WhiteboardLec12.pdf,
Angrist et al. ’96,
Angrist & Pischke Ch. 4,
Imbens & Rubin ’15 Part VI
14 Regression Discontinuity Designs Being scribed See above Angrist & Pischke Ch. 6,
Imbens & Lemieux ’08
15–16 Observational Studies under Ignorability Being scribed WhiteboardLecs15-16.pdf Rosenbaum ’02 Ch. 3,
Imbens & Rubin ’15 Ch. 12
17–18 Matching, Propensity Scores, Double Robustness Being scribed WhiteboardLecs17-18.pdf Rosenbaum ’89, Rosenbaum & Rubin ’83, Kang & Scahfer ’07, Chernozhukov et al. ’16
19–20 Generalized Optimal Matching and Optimal Covariate BalanceBeing scribed WhiteboardLecs19-20.pdf Kallus ’16 HW2, dataset
21–22 Heterogeneous Effects and Policy LearningBeing scribed WhiteboardLecs21-22.pdf Athey & Imbens ’16,Beygelzimer & Langford ’09,Dudik et al. ’11
23–24 Policy Learning and BalanceBeing scribed WhiteboardLecs23-24.pdf Kallus ’17,Athey & Wager ’17

Suggested papers for final presentation


Draft schedule

Lecture Topic
1 Introduction and course overview
2 What is causality and basic elements of statistical learning and decision theory
3 Generalization with known effects: Doob Martingale and McDiarmid's Inequality
4 Generalization with known effects: Rademacher averages, Massart, VC Dimension
5 Unknown effects: introduction to causal inference from controlled experiments
6 Inference on effect: parametric and permutation tests of hypotheses
7 Controlled-experimental design and covariate balance
8 Optimal covariate balance in experimental design
9 Unknown effects without experimental control: introduction to observational data
10 Noncompliance and instrumental variable analysis
11 Causal inference from regression discontinuity designs
12 Causal inference under unconfoundedness: matching and regression
13 Causal inference under unconfoundedness: propensity scores
14 Double robustness and semi-parametric efficiency
15 Generalized optimal matching
16 Generalized optimal matching: analysis
17 Individual-level and heterogeneous causal treatment effects
18 Sequential decision making: lower bounds
19 Sequential decision making: algorithms
20 Sequential decision making: contextual bandits
21 Causal policy learning from observational data
22 Causal policy learning from observational data: extensions
23 Balanced policy learning
24 Balanced policy learning: analysis
25 Fairness in supervised learning
26 Fairness in policy learning
27 Final project presentations
28 Final project presentations