Welcome to the course website dedicated to the PUBL0050 module Causal Inference! On this site, you will find the lecture slides as well as the seminar tasks. You can navigate to each week’s material by clicking on the specific sidebar menu link. Please do let us know if you are struggling to access anything. Below you will find information related to the organisational aspects of the course.

Course Description

This course provides an introduction to statistical methods used for causal inference in the social sciences. We will be concerned with understanding how and when it is possible to make causal claims in empirical research. In particular, using the “potential outcomes” framework of causality, we will focus on understanding which assumptions are necessary for giving research a causal interpretation, and on learning a range of approaches that can be used to establish causality empirically. The course will be practical – in that you can expect to learn how to apply a suite of methods in your own research – and theoretical – in that you can expect to think hard about what it means to make claims of causality in the social sciences.

We will address a variety of topics that are popular in the current political science literature. Topics may include experiments (laboratory, field, and natural); matching; regressions; fixed-effects; difference-in-differences; regression discontinuity designs; instrumental variables; and synthetic control. Examples are drawn from many areas of political science, economics, geography, education, public health, international relations, and public administration.

This course is designed for students in various MSc degree programmes in the Department of Political Science at UCL. It is the second quantitative methods module in a sequence of two, the first being an Introduction to Quantitative Methods which is taught in the Autumn term. This module therefore assumes that students are familiar with the material in the previous module, which covers basic quantitative analysis, sampling, statistical inference, linear regression, regression models for binary outcomes, and some material on panel data.

The goal of the course is to teach students to understand and apply various statistical methods and research designs for answering causal questions in the social sciences. In addition to the theoretical material, students will also learn data analytic skills using the statistical software package R.


Students should have a working knowledge of the methods covered in typical introductory quantitative methods courses (i.e. to the level of PUBL0055 or equivalent). At a minimum, this should include a strong understanding of hypothesis testing and multiple linear regression. You will need to provide me with evidence of having completed at least one prior course that covers this material. There is also an online quiz which you can take to determine whether you are likely to have sufficient knowledge to complete the course.

Students who have not taken PUBL0055 earlier in the year may wish to refresh their knowledge before starting this course. You could consult any of the following textbooks:

  • Imai, Kosuke. 2017. Quantitative Social Science: An Introduction, Princeton University Press
  • Agresti, Alan and Finlay, Barbara. 2009. Statistical Methods for the Social Sciences, Fourth Edition, Pearson International.
  • Stock, James and Watson, Mark. 2015. Introduction to Econometrics, Updated Third Edition, Pearson.

Teaching Format

We recommend you try and structure your weekly workflow as follows:

  1. Complete the required reading
  2. Attend the lecture
  3. Attempt the seminar assignments
  4. Attend the seminar
  5. Go back through the seminar assignment
  6. Throughout the above, make note of things that are still unclear. If they are not clarified by any of the above, send a question via the google forms embedded on the course website or via the Moodle Forum and we will seek to answer these in the next lecture.


Lectures will take place Wednesdays 9-11am, in the IALS Council Chamber, Charles Clore House, 17 Russell Square. The lecture slides will be made available to you to download before the lecture on this website in the tab dedicated to the relevant week.


This is a practical module, and a key learning objective is for students to be able to implement the statistical methods we cover during lectures to real data. Each week, you will complete a problem set which involves writing code in the R programming language (see below for more details) and interpreting the results.

All seminars are held on Fridays. Please stick with your assigned seminar slot, such as to keep an even numbers distribution across the groups. If this is not possible, you can ask the Political Science postgraduate admin team ( for help.

For each seminar, there is a problem-set with questions for you to work on during your seminar. The goal of these seminars is to provide you with ample time to ask questions about the problem set, and particular issues that relate to coding in R. During your allocated seminar time, you will be able to ask questions of the teacher; speak with other students about the problem set; and watch short live demonstrations from your seminar teacher. Attendance during these seminar hours is mandatory and we will take a register at the beginning of the session. However, you will also be able to use Moodle to log questions for teaching staff or other students to answer outside of those allocated hours as well.

Please note that we expect you to have made some attempt to answer the questions in the seminar materials (all hosted on this website) before attending the seminar each week. This will make the seminars themselves much more productive. The solutions will be made visible on the day after the seminars.


Students will be evaluated through a 3000-word research paper applying the methods from the course to a research question chosen by the student. The research paper, which is explained in greater detail in the section “Research Paper”, accounts for 100% of the grade for this module. The research paper is due on April 22nd, 2024 at 2pm. It will be submitted online via Moodle.

  • The research paper should follow the basic elements of a novel research project. The paper should address a specific research question, identify the contribution, provide at least one testable hypothesis, and implement a suitable design based on one of the methods that we study in the course. The paper should focus narrowly on a topic of the student’s choice and display a depth of understanding of one of the approaches discussed on the course, rather than a survey of all methods.-

  • Plagiarism is taken extremely seriously and can disqualify you from the module (for details of what constitutes plagiarism see ). If you are in doubt about any of this, ask me. No late work will be accepted and, given the long-term nature of the research paper, extensions will only be offered in exceptional circumstances.


  • Course website: The main source of information for problem sets, class assignments, and readings will be the course website.
  • Moodle: Other material relevant to the course will be uploaded to the course Moodle site. We will be using a Moodle discussion forum on the course. We encourage you to use this for both student-to-student and student-to-tutor communication, meaning that you should be a) posting questions for other students to answer and b) attempting to answer questions posted by other students. If you ask us a substantive question about the course via e-mail, We will ask you to post it on Moodle so that other students may also benefit

Textbooks and Readings

We primarily use the following textbook on this course:

This book provides an excellent introduction to the potential outcomes framework which forms the conceptual core of this course. It also covers the majority of the methodological approaches that we will study. That said, we will often focus on readings from other books/papers when necessary. The reason that we do not always follow a single textbook is that the field of causal inference is rapidly evolving, and there is no single canonical volume that would cover of all the interesting topics we focus on in this course.

In addition to the textbook treatments, students should read the articles set as “required” reading each week, and it is worth familiarising yourself also with at least some of the “recommended” reading. The required reading will often contain material that is not covered in the textbooks, partly because the methods on this course are at the cutting edge of the discipline and so are (sometimes) too new to have received coverage in textbooks and (often) it is more interesting to read the papers than the book.

The “recommended” readings will typically cover recent or important implementations of the methods we will learn about, and will be helpful in (at least) two regards. First, reading these articles will provide you with an understanding of when the methods we study can provide interesting answers to previously thorny empirical questions. Second, these articles will be helpful templates for the research paper you will write at the conclusion of the course.

Finally, for students who wish to receive a more detailed mathematical exposition of the approaches we will cover on the course, the following books are highly recommended:

Note, however, that these books presents the material in a somewhat less accessible fashion than the 2014 Angrist and Pischke volume, and it is perfectly possible to do well on this module without consulting these more advanced texts. Nevertheless, the relevant chapters are included in the list of “recommended” readings each week.

Other Resources

  • The following is a very useful manual which is structured like a ‘recipe’ book providing solutions to specific problems you may encounter:
  • For a refresher either during this term or over the summer, you may find the following a good alternative to scowering through all your seminar notes during the year.
  • Each R package has online documentation which can be accessed through RStudio itself. Otherwise, they can also be accessed through this website.
  • RStudio provides a number of cheat-sheets here.
  • There is no better help than other users! Therefore, whenever you’re stuck, the RStudio Community, Stack Overflow and Stack Exchange will be your best bet to find an answer.


Throughout the course we will use the free and open source statistical analysis software R. Before the course starts, you can and should download and install R on your personal computer. You should also also download and install RStudio, which is a user-interface to R. Please ensure that both R and RStudio are installed on your personal computers before the first lecture. UCL machines, either virtual via Desktop@UCL or on campus, will already have this software installed.

If you have never used R before, or if you have forgotten everything about it since you last used it, please work through the R Refresher page of this site before the course begins.

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.

Last Updated: 20 Mar 2024 4:34 PM GMT