Models for Cause and Effect: causal inference for social scientists - Online (join a waiting list)

Description:

The fact that correlation does not equate to causation is so well known that it has become a popular saying in itself. Yet the way that quantitative analysis is discussed in much popular and political discourse, as well as interpreted by many social scientists, fails to take issues surrounding causality fully into account. This may be because randomized control experiments, widely understood as the most defensible method of establishing causality, are frequently impossible or unethical to conduct in social science settings.

Analysts thus have to work with observational data, which often miss information crucial for making causal interpretations of statistical associations. However, under some circumstances and subject to specific assumptions, one can interpret estimated associations as casual with substantially higher confidence. This course deals with methods that can be used under such circumstances and subject to the specific assumptions. The course offers practical skills in implementing these methods and the theoretical skills needed to understand and discuss evidence from them.

The course covers:

• An introduction to conceptual issues around causal analysis and counterfactual research design
• A review of “classic” regression and covariate adjustment techniques
• Instrumental variables
• Regression discontinuity
• Difference in difference

By the end of the course participants will be able to:

• Understand the motivation for and theoretical underpinnings of common counterfactual designs
• Discuss strengths and weaknesses of different designs for specific research questions
• Use and interpret output from counterfactual models
• Discuss critically issues of internal and external validity of different designs

This course is aimed at Government researchers and academics and postgraduate students in the social sciences.

Participants should have had an introduction to quantitative research methods at undergraduate or postgraduate level and be familiar with basic concepts in probability theory and statistical inference. Participants should be familiar with basic elements of coding for a statistical software package such as R, SPSS or Stata (preferred). 

We will use Stata and Excel in this course. Participants need to have some familiarity with coding, ideally in Stata, though knowledge of basic coding principles in other packages such as R or SPSS is sufficient. Users will be provided access to Stata for the course.

 

 Preparatory Reading 

The course will draw on the following textbooks:

Morgan, S. L., & Winship, C. (2015). Counterfactuals and causal inference. Cambridge University Press.

Angrist, J. D., & Pischke, J. S. (2014). Mastering'metrics: The path from cause to effect. Princeton University Press.

 

THIS COURSE IS BEING RUN OVER 4 MORNINGS WHICH EQUATES TO TWO TEACHING DAYS FOR PAYMENT PURPOSES.

Programme

Day One

9:00-11:00     Introduction to causal inference and potential outcomes framework, mix of video and online discussion, interactive exercises

11:00-12:00   Regression/covariate adjustment, video and online discussion

12:00-13:00   Lunch

13:00-15:00   Regression / covariate adjustment, Guided lab with chat and video discussion

Day Two

9:00-10:00     Instrumental variables, video and online discussion

10:00-10:30   Half Hour Break

10:30-14:30   Instrumental variables, guided lab with chat and video discussion

Day Three

9:00-10:00      Regression discontinuity, video and online discussion

10:00-10:30    Half Hour Break

10:30-12:30    Regression discontinuity, guided lab with chat and video discussion           

Day Four

9:00-10:00      Differences in Differences, video and online discussion

10:00-10:30    Half Hour Break

10:30-12:30    Differences in Differences, guided lab and online discussion

12:30-13:30    Wrap Up and Evaluations

 

 

 

Cost:

The fee per teaching day is: • £30 per day for UK/EU registered students • £60 per day for staff at UK/EU academic institutions, UK/EU Research Councils researchers, UK/EU public sector staff, staff at UK/EU registered charity organisations and recognised UK/EU research institutions. • £100 per day for all other participants In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. No refunds are available after this date. If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of the cancellation of a course. The University of Southampton’s Online Store T&Cs also continue to apply.

Website and registration:

Register for this course

Region:

South West

Keywords:

Explanatory Research and Causal analysis, Quasi-Experimental Research, Difference-in-differences (DID), Instrumental variables, Regression discontinuity, Secondary Analysis

Related publications and presentations from our eprints archive:

Explanatory Research and Causal analysis
Quasi-Experimental Research
Difference-in-differences (DID)
Instrumental variables
Regression discontinuity
Secondary Analysis

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