IRVAPP ADVANCED SPRING SCHOOL 2019 - Advanced Methods for Impact Evaluation
The school is designed to be particularly beneficial to graduate students and researchers with a high level of proficiency in applied policy evaluation and quantitative micro-econometrics. It will run over three consecutive days, from May 22 to May 24 2019, covering methods for the estimation of policy effects with endogenous participation. Different solutions to the identification problem will be considered, resulting in three thematic sessions.
This is an advanced/graduate course on quantitative empirical methods for policy evaluation. As
such, knowledge of methods for treatment effects and quantitative micro-econometrics
equivalent to Ph.D. or Doctorate level coursework is required.
Machine learning and policy evaluation (Maximilian Kasy)
This part will provide an introduction to some exciting new areas at the intersection of machine learning and policy evaluation. More specifically, it will cover the following topics:
- Taxation, insurance, and machine learning
- Review of optimal tax theory and sufficient statistics
- An introduction to Gaussian Processes for machine learning
- Combining the two
- Experimental design and machine learning for policy choice
- Optimal experimental design for estimating treatment effects
- An introduction to bandit problems
- Adaptive experimental design for policy choice
Discontinuities (Enrico Rettore)
This part will address identification of policy effects when the probability of participation
changes discontinuously or has kinks along observable dimensions. Starting with the basic
methodology and implementation, the module will discuss extensions ranging from multiple cut-
offs, to extrapolation and external validity, and randomization-inference methods.
Instrumental Variables (Erich Battistin)
This part will present solutions when (quasi-)experimental sources of exogenous variability for
treatment participation become available. Identification of “local average” and “marginal”
treatment effects will be discussed, offering a broad overview of ex-ante and ex-post evaluation
methods when effects are heterogeneous across the statistical units.
The course will be taught in English.
Palazzo Loredan, San Marco, 2945, Venice (Italy)
The prestigious palace, restructured in the 16th century, is situated close the Grand Canal and to the Ponte dell’Accademia.
How to reach us
Here you can find more information on how to reach Palazzo Loredan from the train station.
Please note that the fee does not include the accommodation. Find a list of recommended hotels within walking distance from the spring school location in attach at the bottom of this page.
The registration fee includes workshop materials, as well as coffee and lunch breaks; it does not include dinners, accommodation, transport or any other services. We are not in the position to offer any scholarship.
- Students: EUR 500
- HE delegates and Government: EUR 800
- Others: EUR 1200
All bank charges are for the registrant’s account.
Registration to this event is mandatory.Register
The course is limited to 30 participants, to ensure learning effectiveness.
Candidates can enroll at the above link, and will be asked to complete a short application
Online application and payment are due by April 30, 2019. It is advisable that, conditional to the
offer of place, payment is made at the earliest to secure your place on the program. Enrollment
will be secured only after receipt of the payment notification.
Payment shall be done by bank transfer. Details will be provided in the notification of
acceptance, conditional to the offer of place.
Cancellation of registration requests must be notified to firstname.lastname@example.org.
Refund of registration fee will be as follows:
- Cancellation requests received by February 17: full refund.
- Cancellation requests received by March 31: 50% refund.
- Cancellation requests received from April 1 and no-shows: no refund.
Refunds will be made via bank transfer, and all bank charges will be for the registrant’s account.
Participants are responsible for cancelling their own hotel and travel reservations.