Causal Inference in the Social Sciences

The challenge of causal inference is ubiquitous in social science. Nearly every research project fundamentally is about causes and effects.

This introductory session will:

• 1. Introduce three main approaches to elucidate causal relationship: structural equation models, causal directed acyclic graphs, and the counterfactual/potential outcome framework;

• 2. Explain the common challenges in empirical research;

• 3. Talk through some principles and intuition of several research designs that can help researchers make stronger claims for causality.

The emphasis is on setting out applications of each approach, along with pros and cons, so that participants understand when a particular design may be more or less suitable to a research problem.

• University Students from Tier 1 Departments
• Further details regarding eligibility criteria are available here

• Familiarity with basic statistical concepts (such as conditional independence and hypothesis testing) and regression methods.

• Introduce three main approaches to elucidate causal relationship: structural equation models, causal directed acyclic graphs, and the counterfactual/potential outcome framework;

• Explain the common challenges in empirical research;

• Talk through some principles and intuition of several research designs that can help researchers make stronger claims for causality.

• Imbens, G. W., & Rubin, D. B. (2015). Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.

• Pearl, J. (2009) Causality : Models, Reasoning, and Inference (2nd edition) Cambridge University Press.

• Freedman, D. A. (2009) Statistical Models: Theory and Practice. Cambridge University Press.

• Morgan, S. L., & Winship, C. (2015). Counterfactuals and Causal Inference (2nd edition). Cambridge University Press.

• Angrist, J. D., & Pischke, J. S. (2008). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.

• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference.

Houghton Mifflin.

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