Causal Inference: prediction, explanation, and intervention

Location & Time

Fall 2016
Mondays 3:00pm-5:30pm

What is causality and why is it useful? Causes are what allow us to predict what will happen in the future (that a stock price will rise based on a news report), explain why something happened in the past (what actually led to a patient's seizure), and intervene to produce particular outcomes (crafting political speeches to influence voter opinion). Whether you want to buy stocks, develop effective treatments, or manipulate elections, you need to know that you are not acting on a mere surrogate but rather the true culprit.


This course covers the practical tools needed for evaluating causal claims and making causal inferences. We will explore two primary questions -- 1) what is causality? 2) how can we find it? After covering the conceptual and theoretical underpinnings of causal inference, we discuss how causal inference is handled by different fields and how to responsibly test it in real-world cases.

Syllabus [pdf]


None. The course is intended for advanced undergraduate and graduate students from computer science and other disciplines.


Discussion of the readings is an important part of the course and will count towards the final grade. There will be a final project (and presentation), which may be theoretical or experimental in nature (for example, applying causal inference methods to data, writing a critique of a methodology or study, proposing a new inference method).

Grades will be: 5% homework, 15% participation, 30% midterm exam, 50% final project.

Schedule and Lecture notes

See the syllabus for detailed list of readings for each week and due dates

Week 1 (8/29): Introduction to causal inference [slides]
Week 2 (9/12): Regularities, counterfactuals, and token causality[slides]
Week 3 (9/19): Probabilistic causality [slides]
Week 4 (9/26): Intro to graphical models [slides]
Week 5 (10/3): Conditions for inference, BNs [slides]
Week 6 (10/11): Causality in time series: DBNs, logic-based methods [slides]
Week 7 (10/17): Causality in time series: Granger causality [slides]
Week 8 (10/24): Midterm
Week 9 (10/31): Psychology of causality [slides]
Week 10 (11/7): Mechanisms, interventions, and randomized trials (experimentation) [slides]
Week 11 (11/14): Journal club
Week 12 (11/21): Evaluating and making policies [slides]
Week 13 (11/28): Exam
Weel 14 (12/5): Presentations

Note: If you'd like the original powerpoint files to use these slides in your (academic, noncommercial) presentations or teaching, email me at and I'd be happy to send them to you.