FUN

Introduction aux méthodes d’évaluation d’impact des politiques publiques (FUN)

Offered by Université Paris-Dauphine,
Introduction aux méthodes d’évaluation d’impact des politiques publiques (FUN)

Ce MOOC francophone présente les différentes méthodes d’évaluation d’impact des politiques publiques qui se sont développées de manière très significative au niveau académique au cours des 10 dernières années. Ces méthodes sont aujourd’hui mobilisées par de nombreuses institutions pour évaluer l’impact de politiques ou des projets qu’elles promeuvent et financent.

Dans un contexte marqué par une exigence croissante de rendre des comptes sur l’efficacité des dépenses publiques, il apparaît important de sensibiliser tant les décideurs politiques que les agents en charge de la mise en œuvre des politiques aux problèmes posés par la mesure de l’impact des politiques publiques et aux méthodes développées pour les résoudre. Ce besoin est manifeste dans les pays en développement où l'atteinte des Objectifs du Développement Durable (ODD) suppose de sensibiliser tant les experts nationaux et internationaux, acteurs des politiques publiques ou de l'aide internationale, ainsi que les chercheurs en économie ou en sciences sociales, aux techniques d'évaluation d’impact moderne et à leurs exigences.

Ce MOOC s’appuiera sur 6 modules constitués de séquences vidéos et de quiz qui présenteront les aspects techniques de mise en œuvre ainsi que les conditions de validité des différentes méthodes d’évaluation d’impact, tant expérimentales que non expérimentales.

Plan du cours

Le cours se déroule sur six semaines :
Semaine 1 : Le problème de l’évaluation d’impact
Semaine 2 : Les méthodes expérimentales (Randomized Controlled Trials)
Semaine 3 : Variables instrumentales
Semaine 4 : Régressions sur discontinuités
Semaine 5 : Double différence
Semaine 6 : Appariement

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Introduction to Statistics (Coursera) Coursera
Stanford University

Introduction to Statistics (Coursera)

Statistics & Data Analysis Data Science

Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.

Nov 2nd 2026
5-12 Weeks
Statistics Analysis Probability
Details
Foundations of Sports Analytics: Data, Representation, and Models in Sports (Coursera) Coursera
University of Michigan

Foundations of Sports Analytics: Data, Representation, and Models in Sports (Coursera)

Statistics & Data Analysis Data Science

This course provides an introduction to using Python to analyze team performance in sports. Learners will discover a variety of techniques that can be used to represent sports data and how to extract narratives based on these analytical techniques. The main focus of the introduction will be on the use of regression analysis to analyze team and player performance data, using examples drawn from the National Football League (NFL), the National Basketball Association (NBA), the National Hockey League (NHL), the English Premier LEague (EPL, soccer) and the Indian Premier League (IPL, cricket).

Oct 26th 2026
5-12 Weeks
Python Regression Sports
Details
Data Science: Capstone (edX) EdX
HarvardX,Harvard University

Data Science: Capstone (edX)

Statistics & Data Analysis Data Science

Show what you’ve learned from the Professional Certificate Program in Data Science. To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Self Paced
Self-Paced
Probability Machine Learning Regression
Details
Data Science Companion (Coursera) Coursera
MathWorks

Data Science Companion (Coursera)

Statistics & Data Analysis Data Science

The Data Science Companion provides an introduction to data science. You will gain a quick background in data science and core machine learning concepts, such as regression and classification. You’ll be introduced to the practical knowledge of data processing and visualization using low-code solutions, as well as an overview of the ways to integrate multiple tools effectively to solve data science problems.

Oct 23rd 2026
4 Weeks
MATLAB Machine Learning Regression
Details
Regression Models (Coursera) Coursera
Johns Hopkins University

Regression Models (Coursera)

Statistics & Data Analysis Data Science

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models.

Oct 19th 2026
4 Weeks
Statistics Regression Linear Regression
Details
Train Machine Learning Models (Coursera) Coursera
CertNexus

Train Machine Learning Models (Coursera)

Data Science

This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems. To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.

Oct 26th 2026
5-12 Weeks
ML Machine Learning Clustering
Details
Engineering Life: Synbio, Bioethics & Public Policy (Coursera) Coursera
Johns Hopkins University

Engineering Life: Synbio, Bioethics & Public Policy (Coursera)

Sci: Biology & Life Sciences Health & Society

Synbio is a diverse field with diverse applications, and the different contexts (e.g., gain-of-function research, biofuels) raise different ethical and governance challenges. The objective of this course is to increase learners’ awareness and understanding of ethical and policy/governance issues that arise in the design, conduct and application of synthetic biology. The course will begin with a short history of recombinant DNA technology and how governance of that science developed and evolved, and progress through a series of areas of application of synbio.

Nov 2nd 2026
5-12 Weeks
Biology Bioethics Public Health
Details
Machine Learning: Regression (Coursera) Coursera
University of Washington

Machine Learning: Regression (Coursera)

Statistics & Data Analysis Data Science

Case Study - Predicting Housing Prices. In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.

Oct 19th 2026
5-12 Weeks
Python Algorithms Machine Learning
Details