Participant profile

PhD students at the University of the Basque Country (EHU)

Calendar

Biscay Campus: October/November/December 2026

Duration / Timetable

20 hours (4-hour lectures running over 5 weeks)

Time: 9:00 to 13:00

Attendance Requirement

Lecture attendance is mandatory and students are expected to complete weekly assignments (see points 3 and 5 of the Basic regulations for participation in transversal training activities organised by the Doctoral School).

ALL ABSENCES must be justified with supporting documentation.

Language

English

Modality

Face-to-face

Pre-requisites

Knowledge of basic probability theory and regression methods will be assumed.

Location and dates

Trainer

Javier Gardeazabal: professor at the EHU. See CV.

Group size

20

Registration

Competences to be acquired by the doctoral student (Royal Decree 99/2011)

• a) Systematic understanding of a field of study and mastery of research skills and methods related to that field.
• b) Ability to conceive, design or create, implement and adopt a substantial process of research or creation.
• c) Ability to contribute to the expansion of the frontiers of knowledge through original research.
• e) Ability to communicate with the academic and scientific community and with society in general about their fields of knowledge in the modes and languages in common use in their international scientific community.
• f) Ability to promote, in academic and professional contexts, scientific, technological, social, artistic or cultural progress within a knowledge-based society.

Objectives

The objective of the course is to learn how to apply various causal inference methods to applied research questions. To attain this objective, students will be asked to apply the methods to real data sets to show they are able to understand the methods, apply the estimation procedures and interpret the empirical results.

Format

We will meet once a week on Wednesdays from 9:00 to 13:00. Lectures will have two parts. In the first part I will teach the methods, and after a short break, in the second part, students will apply those methods to actual data provided in class. Additionally, students will be asked to apply the methods to their own research questions and data sets.

Content

Causal Inference covers methods to establish causal relationships between a treatment, policy or intervention and an outcome or endogenous variable using different types of data: experimental and observational data. Causal inference has applications in virtually all scientific fields, including experimental and social sciences. First, we will review the methods used to deal with data from randomized experiments, the so called Randomized Control Trials (RCTs). Then, we will extend the methods to cover the case when data are observational, i.e. the data are not from a RCT. A particularly important application of causal inference is the evaluation of public programs, interventions and policies. These methods allow the researcher to determine whether a treatment, policy or program has the intended effect in a quantitatively sound manner.

More in detail, the contents of the course include:

• Causal inference for randomized experiments
• Propensity score methods
• Matching methods
• Instrumental Variables
• Difference-in-differences and synthetic controls