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Measuring Causal Effects in the Social Sciences

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты
Статус:
Курс по выбору
Когда читается:
2-й курс, 1 модуль

Course Syllabus

Abstract

How can we know if the differences in wages between men and women are caused by discrimination or differences in background characteristics? In this PhD-level course we look at causal effects as opposed to spurious relationships. We will discuss how they can be identified in the social sciences using quantitative data, and describe how this can help us understand social mechanisms. The course is a Massive Open Online Course delivered at Coursera platform ( online courses and take an oral examination at HSE for completing the course. The examination is taken after completion of the course during examination week. The full syllabus is published at the course websites. (https://www.coursera.org/learn/causal-effects). The course doesn’t require special previous knowledge and competences. Only for students of Comparative Social Research programme
Learning Objectives

Learning Objectives

  • Provide students the instruments on how to measure the causal effects
  • to introduce the multivariate regression model and the concept of mediating factors
  • go through the concept of instrumental variables.
Expected Learning Outcomes

Expected Learning Outcomes

  • to get familiar with the nature of causal effects and how to measure it
  • how to measure causal effects in the social sciences
  • understand the causality and the randomzied controlled trial.
Course Contents

Course Contents

  • Week 1. The Nature of Causal Effects and How to Measure Them
    Thıs week we are looking at the nature of causal effects and how to measure them.
  • Week 2. The Multivariate Regression Model and Mediating Factors
    This second module introduces you multivariate regression model and the concept of mediating factors.
  • Week 3. Randomized Controlled Trials
    In this third week of the course we are having a closer look at causality and the randomzied controlled trial.
  • Week 4. Instrumental Variables
    The fourth week of the course we will go through the concept of instrumental variables.
Assessment Elements

Assessment Elements

  • Partially blocks (final) grade/grade calculation After attending the MOOC it is required to present the final results (certificate/another document)
  • non-blocking Oral exam
  • Partially blocks (final) grade/grade calculation After attending the MOOC it is required to present the final results (certificate/another document)
  • non-blocking Oral exam
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    After attending the MOOC it is required to present the final results (certificate or another document - C). The document has to be submitted to the study office immediately after completion of the course. After successful completion of the course an examination is undertaken. Prerequisite for attending the examination is submission of the certificate to the study office. The examination grade (E) is the final grade for the course. Final control: oral group exam. The overall course grade (G) (10-point scale) is calculated as a sum of G = C*0.7+ E*0.3
Bibliography

Bibliography

Recommended Core Bibliography

  • An introduction to multivariate statistical analysis, Anderson, T. W., 2003
  • J. Scott Long, & Jeremy Freese. (2006). Regression Models for Categorical Dependent Variables using Stata, 2nd Edition. StataCorp LP. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.tsj.spbook.long2
  • Matched sampling for causal effects, Rubin, D. B., 2008
  • Multivariate analysis techniques in social science research : from problem to analysis, Tacg, J., 1997
  • Multivariate total quality control : foundation and recent advances, Lauro, C., 2002

Recommended Additional Bibliography

  • Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error. (2019). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C9B29B32
  • Rubin, D. B. (2006). Matched Sampling for Causal Effects. Cambridge: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=171872
  • Statistical graphics for visualizing multivariate data, Jacoby, W. G., 1998