Course Name: Data, Evidence and Public Policy
Course Number: PUBL0097
Lecture: Thursday, 10:00-11:00 AM – Rooms
Seminar: Friday, 1:00-2:00 PM, 2:00-3:00 PM and 3:00-4:00 PM – Rooms
Credits: 15

Instructor: Moritz Marbach
Office: 36-38 Gordon Square, Room 303
Office Hours: Office 365 Bookings


This course is about how the public sector use experimentation, big data, and machine learning to design and implement policy. Its central objective is to show how adopting evidence-informed and data-driven public policies has the potential to help but also to harm citizens. Rather than fostering deep technical understanding of machine learning, big data analytics, and experimentation, this course aims to equip students with a conceptual understanding sufficient to recognize challenges and opportunities. It draws on examples of local and national governments using experimentation, big data, and machine learning to deliver public services and allocate scarce resources. For example, we look at how satellite imagery and randomized control trials are being used and misused in fighting poverty; how machine learning amplifies and limits discrimination in market settings; and how large-scale data collections are used by authoritarian governments to repress dissent. We also discuss the historical origins of evidence-informed public policymaking, and barriers to the use of evidence in the public sector.

Meeting Schedule

Date Week Topic
05.10.22 1 Evidence-Informed Public Policy
12.10.22 2 Randomized Control Trials (RCTs)
19.10.22 3 RCTs and Their Critics
26.10.22 4 Science and Publication Bias
02.11.22 5 Prediction and Causation
09.11.22 (UCL Reading Week)
16.11.22 6 Amplifying Inequality
23.11.22 7 Fighting Discrimination
30.11.22 8 Big Data and to Help
07.12.22 9 Big Data to Harm
14.12.22 10 Barriers to Evidence-Use

Learning Outcomes

  1. Perform a review of the evidence base on a policy
  2. Summarize social science studies evaluating a policy
  3. Identify barriers to evidence-use in the public sector
  4. Recognize potential use cases for experimentation, big data, and ML in the public sector
  5. Critically discuss applications of experimentation, big data, and ML in the public sector


To complete this course, you have to submit a review of the available scientific evidence on a policy question. The review cannot exceed 3,000 words in total. Details can be found here.

If you experience any difficulties that mean you are not able to study to the best of your ability and struggle to meet deadlines, then you should speak to your personal tutor for help filling out and submitting an Extenuating Circumstances Form.

Class Assignments

Before each class, you are expected to review the material on the course website. It is critical that you complete the required readings before we meet. Each week comes with guiding questions and brief instructions on how to prepare for our meeting. Take notes while you read and think about how the material speaks to the guiding questions.


There are no formal requirements for this course, but I will assume that students either take the course Statistical Learning for Public Policy (ECON0127) in parallel or have already completed a course in statistical learning.

Academic Freedom

Academic freedom is the cornerstone of university research and teaching, so that all university staff, speakers, and students can freely explore questions and ideas and challenge perceived views and opinions, without being censored or harassed by a government, any state authorities, the University, other students, or external pressure groups. As part of the UCL academic community, all staff, speakers, and students share these responsibilities:

  • Everyone must respect freedom of thought and freedom of expression. Your lecturer will not limit what can be discussed in the seminar, as long as it is relevant to the subject. They will not censor any topics, and they will expose you to controversial issues, questions, facts, views, and debates.

    • You may disagree with some facts or views that you read or hear in the classroom. You are encouraged to engage with these facts and views in a respectful manner.

    • Your lecturer will not penalise you merely for expressing views they or other students disagree with. However, they will expect you to present logical arguments supported by evidence.

  • You are explicitly prohibited from recording, publishing, distributing or transferring any class material/content, in whole or in part, in any format, to any individual or entity outside the module, linking to or posting it online (including social media), or making it otherwise available to any person or entity outside the module, unless you have received prior specific written approval from the module leader. You are also explicitly prohibited from aiding or abetting in any of these actions. Similarly, your lecturer will not record, publish or distribute seminar sessions without the explicit consent of the participants.

By agreeing to take this module, you agree to abide by these terms. If you do not comply with these terms, you will potentially be subject to disciplinary actions similar to those under violations of the university Student Code of Conduct.