Fausto GALLI | REGULATORY ECONOMY AND PROGRAM EVALUATION
Fausto GALLI REGULATORY ECONOMY AND PROGRAM EVALUATION
cod. 0212800013
REGULATORY ECONOMY AND PROGRAM EVALUATION
| 0212800013 | |
| DEPARTMENT OF ECONOMICS AND STATISTICS | |
| EQF6 | |
| STATISTICS FOR BIG DATA | |
| 2024/2025 |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2018 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| SECS-P/03 | 10 | 60 | LESSONS |
| Exam | Date | Session | |
|---|---|---|---|
| GALLI | 10/12/2024 - 16:00 | SESSIONE ORDINARIA | |
| GALLI | 10/12/2024 - 16:00 | SESSIONE DI RECUPERO |
| Objectives | |
|---|---|
| KNOWLEDGE AND UNDERSTANDING: STUDENTS WILL ACQUIRE AN IN-DEPTH UNDERSTANDING OF CONCEPTS AND MODELS RELATED TO CAUSALITY AND POLICY EVALUATION. THEY WILL BE ABLE TO COMPREHEND AND INTERPRET THE MAIN STATISTICAL METHODS USED FOR CAUSAL EFFECT ANALYSIS. APPLICATION OF KNOWLEDGE AND UNDERSTANDING: APPLYING KNOWLEDGE AND UNDERSTANDING: STUDENTS WILL BE ABLE TO CRITICALLY APPLY CAUSAL MODELS AND POLICY EVALUATION TECHNIQUES TO REAL-WORLD CONTEXTS. THEY WILL BE CAPABLE OF SELECTING AND CORRECTLY APPLYING THE MOST APPROPRIATE STATISTICAL TECHNIQUES TO ASSESS THE IMPACT OF PUBLIC POLICIES. AUTONOMY OF JUDGEMENT: MAKING JUDGEMENTS: STUDENTS WILL DEVELOP THE ABILITY TO CRITICALLY EVALUATE EMPIRICAL EVIDENCE AND POLICY EVALUATION STUDIES, IDENTIFYING THEIR LIMITATIONS AND IMPLICATIONS FOR POLICY FORMULATION. COMMUNICATION SKILLS: COMMUNICATION SKILLS: STUDENTS WILL BE ABLE TO COMMUNICATE THE RESULTS OF POLICY EVALUATIONS CLEARLY AND PERSUASIVELY, BOTH VERBALLY AND IN WRITING, TO BOTH TECHNICAL AND NON-TECHNICAL AUDIENCES. AUTONOMOUS LEARNING SKILLS: LEARNING SKILLS: STUDENTS WILL BE ENCOURAGED TO DEVELOP AUTONOMOUS RESEARCH SKILLS AND A CONTINUOUS LEARNING APPROACH IN THE FIELD OF POLICY EVALUATION, THROUGH CRITICAL ANALYSIS OF SCIENTIFIC LITERATURE AND PRACTICAL APPLICATION OF LEARNED CONCEPTS. |
| Prerequisites | |
|---|---|
| KNOWLEDGE OF THE MAIN CONCEPTS OF DESCRIPTIVE AND INFERENTIAL STATISTICS, IN PARTICULAR: RANDOM VARIABLES, ESTIMATORS AND THEIR MAIN PROPERTIES, HYPOTHESIS TESTING. KNOWLEDGE OF THE MAIN CONCEPTS OF LINEAR REGRESSION ANALYSIS. |
| Contents | |
|---|---|
| 1. CAUSALITY - DIRECTED ACYCLIC GRAPHS - POTENTIAL OUTCOMES 2. EXPERIMENTS 3. SELECTION ON OBSERVABLES - REGRESSION - MATCHING 4. SELECTION ON UNOBSERVABLES - REGRESSION DISCONTINUITY DESIGN (RDD) - INSTRUMENTAL VARIABLES - DIFF-IN-DIFF - PANEL DATA - SYNTHETIC CONTROL GROUP |
| Teaching Methods | |
|---|---|
| 60 HOURS OF LECTURES (10 CFU), TAUGHT IN CLASS WITH THE AID OF PROJECTIONS AND APPLIED SOFTWARE. ATTENDANCE IS STRONGLY RECOMMENDED BUT NOT COMPULSORY. IN CASE OF RESTRICTIONS TO ATTENDANCE DUE TO SECURITY MEASURES, CLASSES WILL BE HELD ONLINE. |
| Verification of learning | |
|---|---|
| THE FINAL EXAM WILL BE ORAL. THE INTERVIEW WILL COVER A SERIES OF TOPICS (FROM 3 TO 5) DISCUSSED DURING THE COURSE AND RANDOMLY CHOSEN BY THE INSTRUCTOR. IN ADDITION, STUDENTS MAY CHOOSE TO GIVE A PRESENTATION OF A PROJECT DURING THE COURSE. THIS OPTIONAL PRESENTATION WILL HAVE A 50% IMPACT ON THE FINAL GRADE." |
| Texts | |
|---|---|
| CUNNINGHAM, SCOTT. CAUSAL INFERENCE: THE MIXTAPE. YALE UNIVERSITY PRESS, 2021. |
| More Information | |
|---|---|
| THE EXERCISES WILL BE CONDUCTED USING THE R PROGRAMMING LANGUAGE. |
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