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Causal Inference with Natural Experiments: DiD, RDD, IV, and Matched Designs (RSS2.05) - Closed

Correlation is not causation, but when can we give causal interpretations after analysing non-experimental data? This course introduces quantitative causal inference using observational data in the social and economic sciences. You discuss the complexities of causal research, modern methods that can enable estimating effects of phenomena that were not randomized, and limitations in doing so.

Duration: one-week.

    General

     

    This course is closed, registration is no longer possible. 

    Correlation is not causation, but when can you give causal interpretations after analysing non-experimental data? 
    This course introduces quantitative causal inference using observational data in the social and economic sciences. You discuss the complexities of causal research, modern methods that can enable estimating effects of phenomena that were not randomized, and limitations in doing so. You will investigate a variety of specific topics in causal inference, including the design of observational studies, potential outcomes, difference-in-differences designs, discontinuity designs, matching, instrumental variables analysis for non-compliance, synthetic control methods, difference-in-differences designs with many time periods, formal sensitivity analysis, and other special topics as time and your interest permit, including causal mediation (the analysis of mechanisms) and dynamic or time-varying treatments. 
    You will learn how to implement these methods using the R statistical language.

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    Starting date

    24 June 2024, 9 am
    Educational method
    On-site
    Main Language
    English
    Sessions
    24 June 2024, 9 am - 28 June 2024, 5 pm
    Teacher(s)
    Ryan T. Moore
    Unique code
    RSS2.05

    Factsheet

    Type of education
    Course
    Entry requirements
    See the requirements in cost and admission
    Study load (ECTS)
    2
    Result
    Certificate
    Organisation
    Radboud Summer School

    Total package & social events

    Ryan T. Moore

    Ryan T. Moore
     

    Ryan T. Moore is associate professor in the Department of Government at American University in Washington, DC (AU), where he is also associate director of the Center for Data Science. He serves as Senior Social Scientist with The Lab at DC, and Fellow in Methodology with the US Office of Evaluation Sciences. He earned his Ph.D. from the Department of Government at Harvard University. His research interests center around statistical political methodology, with applications in social policy and evidence-based policymaking. Methodologically, he develops and implements methods for political experiments, causal inference, ecological data, missing data, and geolocated data.

    This course is closed, registration is no longer possible. 

     

    Costs

    • Regular: €1049 (application deadline 1st of May)
    • Student & PhD's: €699 (application deadline 1st of May)

    Includes: your course, short morning and late afternoon courses, coffee and tea during breaks, a warm lunch every day, Official Opening, MethodsNET Café (including some drinks and snacks) Official Closing (with some drinks and snacks) and a 1-year (2024 calendar year) free membership as MethodsNET regular member.

    Excludes: transport, accommodation, social events and other costs. 

    Discounts and Scholarships

    Admission

    Level of participant: 

    • Master
    • PhD
    • Postdoc
    • Professional

    Admission requirements: 

    We expect that students will be have encountered introductory inferential statistics (such as tests of statistical significance) and perhaps the basics of the linear model. The course will employ R as the language of analysis, but will support the interpretation of R code for those with background in STATA, SPSS, Python, or similar.

    Admission documents: 

    None