• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
For visually-impairedUser profile (HSE staff only)Search
Master 2020/2021

A Crash Course in Causality: Inferring Causal Effects from Observational Data

Type: Elective course (Evidence-based Educational Policy)
Area of studies: Public Administration
When: 2 year, 3 module
Mode of studies: distance learning
Open to: students of one campus
Instructors: Ksenia Romanenko
Master’s programme: Доказательная образовательная политика
Language: English
ECTS credits: 3

Course Syllabus

Abstract

We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). So join us and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study
Learning Objectives

Learning Objectives

  • To learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods
Expected Learning Outcomes

Expected Learning Outcomes

  • Define causal effects using potential outcomes
  • Describe the difference between association and causation
  • Express assumptions with causal graphs
  • Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting)
  • Identify which causal assumptions are necessary for each type of statistical method 
Course Contents

Course Contents

  • The Nature of Causal Effects and How to Measure Them
  • Randomized Controlled Trials
  • Instrumental Variables
  • Difference in Difference
  • The Multivariate Regression Model and Mediating Factors
Assessment Elements

Assessment Elements

  • non-blocking Рефлексивное эссе
  • non-blocking Финальный экзамен
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.4 * Рефлексивное эссе + 0.6 * Финальный экзамен
Bibliography

Bibliography

Recommended Core Bibliography

  • Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education (Vol. Eighth edition). New York: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1614634
  • The Use of Instrumental Variables in Higher Education Research (Vol. 6). (2020). Emerald Insight. https://doi.org/10.1108/s2056-375220200000006005

Recommended Additional Bibliography

  • Taylor-Rodriguez, D., Lovitz, D., Mattek, N., Wu, C.-Y., Dodge, H., Kaye, J., & Jedynak, B. M. (2020). Unstructured Primary Outcome in Randomized Controlled Trials.
  • Uriel Cohen Priva, & Chelsea Sanker. (2019). Limitations of difference-in-difference for measuring convergence. https://doi.org/10.5334/labphon.200