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.

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

Register in Studentweb

Candidates from outside UiO, please contact Guro Schmidt Øvregard

Prerequisites

Formal prerequisite knowledge

 

Overlapping courses

10 credits overlap with STV4027 - Quantitative Causal Analysis and Prediction

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 pass/fail scale. Read more about the grading system.

Facts about this course

Credits

10

Level

PhD

Teaching

24 Jan - 24 Feb 2017

Teaching language

English