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Causal Inference

Dates:
  • Mon 20 Jan 2020 09.00 - 11.00
  Add to Calendar 2020-01-20 9:00 2020-01-20 11:00 Europe/Paris Causal Inference

Content Summary and Learning Outcomes

The objective is to learn how statistical methods can help us to draw causal claims about phenomena of interest. During the five-days of the course, participants will be introduced into an authoritative framework of causal inference in social sciences, i.e. the potential outcomes framework. By the end of the course, students will be in position to:

1. critically read and evaluate statements about causal relationships based on some analysis of data;

2. apply a variety of design-based easy-to-implement methods that will help them draw causal

inferences in their own research.

3. think about archival data under the logic of causal inference.

Either explicitly or implicitly, the goal of most empirical research is to interpret causally the co-occurrence of interesting phenomena. Addressing causality, however, has been notoriously difficult without the luxury of experimental data. This course will introduce you to methods that allow you to make convincing causal claims without working with experimental data. We will look at three such designs:

1. Difference-in-Differences estimation;

2. Instrumental Variables and;

3. Regression Discontinuity Design

For every method, the following structure will be employed: first, a running example will provide the motivation and intuition. We will then proceed with the formal identification derivation and finally we will focus on estimation strategies and robustness checks. For each method there will be a hands-on lab section, where we will apply these methods with real data.

Depending on how much time we will have available in the course, we will also cover three additional topics:

1. Matching; and

2. Synthetic Control

Seminar Room 2 - Badia Fiesolana DD/MM/YYYY
  Seminar Room 2 - Badia Fiesolana

Content Summary and Learning Outcomes

The objective is to learn how statistical methods can help us to draw causal claims about phenomena of interest. During the five-days of the course, participants will be introduced into an authoritative framework of causal inference in social sciences, i.e. the potential outcomes framework. By the end of the course, students will be in position to:

1. critically read and evaluate statements about causal relationships based on some analysis of data;

2. apply a variety of design-based easy-to-implement methods that will help them draw causal

inferences in their own research.

3. think about archival data under the logic of causal inference.

Either explicitly or implicitly, the goal of most empirical research is to interpret causally the co-occurrence of interesting phenomena. Addressing causality, however, has been notoriously difficult without the luxury of experimental data. This course will introduce you to methods that allow you to make convincing causal claims without working with experimental data. We will look at three such designs:

1. Difference-in-Differences estimation;

2. Instrumental Variables and;

3. Regression Discontinuity Design

For every method, the following structure will be employed: first, a running example will provide the motivation and intuition. We will then proceed with the formal identification derivation and finally we will focus on estimation strategies and robustness checks. For each method there will be a hands-on lab section, where we will apply these methods with real data.

Depending on how much time we will have available in the course, we will also cover three additional topics:

1. Matching; and

2. Synthetic Control


Location:
Seminar Room 2 - Badia Fiesolana

Affiliation:
Department of Political and Social Sciences

Type:
Seminar

Contact:
Jennifer Rose Dari (EUI - Department of Political and Social Sciences) - Send a mail

Organiser:
Prof. Elias Dinas (EUI - Department of Political and Social Sciences)

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