STV4027 – Quantitative Causal Analysis and Prediction
Course description
Course content
This course introduces students to recent developments in the scholarly effort to derive causal explanations using quantitative methods. The bulk of the course will be concerned with how to identify and estimate causal effects in observational studies. It can be argued that this effort amounts to a paradigm shift within quantitative social science away from regression models and explained variance to identification and measurement of causal effects.
Taking the randomized experiment as the ideal, we clarify the challenges faced by social scientists seeking to draw causal inferences from observational data. Units in observational studies usually select into their causal status (their ”treatment” status) through processes outside of the control of the researcher rather than being assigned to these causal states by the researcher, such as in controlled experiments. The characteristics of this selection process are central throughout the course. We present a range of approaches for identifying its’ core features and for drawing valid causal inferences given those features. In the process, we highlight the limitations and difficulties associated with causal estimates obtained via the different techniques. More than anything, the course aims to develop a critical, yet constructive, mindset towards claims of causal effects in observational studies
The course will also give a brief introduction to basic techniques and concepts used for prediction purposes, and discuss how prediction differs from, and relates to, causal explanation. Together, causal inference and prediction constitute the two main activities of contemporary quantitative social science, and students of this course will be familiarized with the challenges and promises of both of them.
Former title (before spring 2017): STV4027 - Causal Inference.
Learning outcome
Knowledge
- Understanding of the fundamental challenge of drawing causal inferences from observational data
- Overview of recent methodological developments for drawing causal inference from observational data
- Familiarity with the literature on causal inference within political science
- Understanding of the basic difference between causal inference and prediction methods, and overview of mainstream applied prediction analysis in political science
Skills
- Ability to design research projects capable of capturing causal effects
- Ability to identify suitable techniques for causal inference
- Ability to address potential challenges to the validity of the results
- Alertness to the limitations of the inferences drawn
- Ability to use prediction methods
Competences
- Familiarity with statistical techniques for causal inference (and prediction)
- Capability of writing academic texts in a short and concise manner
Admission
Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.
Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.
If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.
Apply for guest student status if you are admitted to another Master's programme.
Prerequisites
Formal prerequisite knowledge
Teaching
Lectures.
There will be two lectures each week. Students are expected to do the readings prior to the lectures, and to have familiarized themselves with the relevant datasets and software prior to doing the weekly assignments.
Examination
Portfolio examination.
The course will be assessed through a series of weekly written assignments (1 per week), 5 in total. Each assignment will count equally towards the final grade.
Language of examination
The examination text is given in English, and you submit your response in English.
Grading scale
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Explanations and appeals
Resit an examination
If you are sick or have another valid reason for not attending the regular exam, we offer a postponed exam later in the same semester.
See also our information about resitting an exam.
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
Evaluation
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.