(C) The Conversation This unaltered story was originally published on TheConversation.com/us [1] License: Creative Commons - CC BY-ND 4.0 Attributions/No Derivities[2] ---------------- Models for Cause and Effect: causal inference for social scientists By: [] Date: 2021-10 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 [END] [1] URL: https://theconversation.com/uk/events/models-for-cause-and-effect-causal-inference-for-social-scientists-9862 [2] URL: https://creativecommons.org/licenses/by-nd/4.0/ TheConversation via Magical Fish Gopher New Feeds: gopher://magical.fish/1/feeds/news/theconversation/