MSc Data Science (Social Analytics) / Course details

Year of entry: 2021

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Course unit details:
Quantitative Evaluation of Policies, Interventions and Experiments.

Unit code SOST70172
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Social Statistics
Available as a free choice unit? Yes


Researchers, government and policy takers, business leaders and people in general are motivated by “causal questions” of the type “Does X cause Y” (e.g does policing reduce crime? Do minimum wages increase unemployment? Does a new educational innovation increase educational achievement? Does a new policy reduce waiting lists in hospitals? Does expenditure in marketing increase sales? Dos affirmative action reduce discrimination?). Standard statistical methods, regardless of their complexity, cannot answer these questions on their own and a new set of statistical tools are needed.

This unit introduces the modern methods of causal inference. You will learn Rubin’s Potential Outcomes framework, and how to use this framework to clarify what data can tell you about a causal effect of interest. You will learn various methods to estimate causal effects from observational and experimental data. Critically, you will be able to gain a deep understanding of the role that different assumptions play in determining what one can learn from data regarding causal questions. The skills you can acquire in this course are applicable to explore causal questions and undertake policy evaluation in a myriad of fields, including economics, criminology, sociology and politics, development, medicine, epidemiology or psychology, to mention but a few.





Specifically, by completing this module, you will

·        Learn to estimate causal effects and answer causal questions in a rich variety of situations ranging from experimental settings to observational data from irregular assignment mechanisms

·        Understand the role played by assumptions in the identification of causal effects in different settings

·        Become acquaintance with a wide range of estimation and inferential methods for causal models in design based setting (e.g. instrumental variables, regression discontinuity, difference in difference) and model based settings (panel data, matching methods) as well as some more advanced techniques (principal stratification and partial identification)

·        You will learn to use the free software R to implement those statistical methods


Learning outcomes

Student should, at the end of this course, be able to

Select, among a pool of competing methods, those most appropriate to estimate the effect of a policy, experiment or intervention on an outcome of interest.

Implement the selected estimator using widely available software such as R.

Successfully write a report explaining and supporting the findings of their analyses.

Conditional on having a clear policy or research question, students will be able to design policies, experiments and interventions to estimate causal effects.


Teaching and learning methods

Teaching will be based on asynchronous lectures, regular exercises, and a weekly live session.

Please note the information in scheduled activity hours are for guidance only and may change.


Knowledge and understanding




Intellectual skills



Practical skills




Transferable skills and personal qualities

Transferable skills and personal qualities: 

Be able to pursue research to unveil causal effects.

Be able to inform debates and assist in the design and implementation of policies, pilot projects, experiments and other projects aiming at revealing a causal effect.

Be able to write a blog or policy advice in relation to quantitative data from randomised experiments.

Progress towards more formal and technically oriented courses in the area of Causal Inference.



Assessment methods

Assessment task

Length required

Weighting within unit

Written assignment

2000 words


Set Exercise

Tutorial exercises






Recommended reading

Indicative reading:

There is not, at present, a textbook on the topic of causal inference/policy evaluation aimed at students with a low-to-intermediate level of statistics (though perhaps “Mastering metrics” by Angrist and Pischke might provide a template for future developments). Therefore, lecture notes, gauged at the expected level of the audience, will be provided to students.  These notes will be based on the indicative readings.

Most of the empirical papers listed are suitable for broad audiences and students will be able to read them after  introducing the corresponding topic in the classes.  When possible, data from the papers will be used in the tutorials for replication.

Some of the indicative readings more than exceed the level of the course in terms of complexity (for example, Angrist, Imbens and Rubin, 1996), and are provided for completeness.

Some textbooks/textbook-length articles on the topic of causal inference (which will often be above the level of this course)  but which provide useful passages are:

  • Winship, Christopher, and Stephen L. Morgan. 1999. The estimation of causal effects from observational data. Annual Review of Sociology 25: 659-706.
  • Angrist and Pischke (2014) Mastering metrics: the path from cause to effect, Princeton University Press.
  • Morgan and Winship, (2015). Counterfactual and Causal Inference: Methods and Principles for Social Science Research. Cambridge University Press.
  • Angrist and Pischke (2008) Mostly Harmless Econometrics, Princeton University Press.


In addition to these, the background statistics and probability required can be sourced from a number of books:

  • Bartholomew, D. J. (2016). Statistics without the maths, Sage Publishing.
  • Dancey, C. and Reidy,  J. (2017). Statistics without the maths for psychology. Prentice Hall.
  • Barrow, M. (2017) Statistics for Economics, Accounting and Business Studies, Pearson


Topic by topic (this list does not currently describe materials on a week-by-week basis)

  1. Introduction to the potential outcomes framework.  Confounding. Causal Effects.
    1. Angrist and Pischke (2008), chapter 1.
    2. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688-701.
    3. Messerli, F. (2012) Chocolate Consumption, Cognitive Function, and Nobel Laureates,  New England Journal of Medicine 367(16):1562-4 · October 2012.
    4. Lawlor, D.A., Smith, G.D., Bruckdorfer, K.R., Kundo, D., Ebrahim, S. (2004) Those confounded vitamins: What can we learn from the differences between observational versus randomized trial evidence? Lancet 363, 1724–1727.


  1. Randomized control trials and Field Experiments.
    1. Lind, J. (1772) A treatise on the scurvy, 3rd Edition. Sands, Murray and Cochran.
    2. Sanders, Charles and Jastrow, Joseph (1885) On Small Differences in Sensation, Memoirs of the National Academy of Sciences, 3, 73-83 (
    3. Vohs, K., Mead, N., and Goode, M. (2006) The psychological consequences of money. Science, 314, Issue 5802, 1154-1156.
    4. Yip Winnie, Powell-Jackson Timothy, Chen Wen, Hu Min, Fé Eduardo, Hu Mu, Jian Weiyan, Lu Ming, Han Wei, Hsiao William C. (2014) Capitation combined with pay-for-performance improves antibiotic prescribing practices in rural China. Health Affairs Vol 33, pp. 502-510.
    5. Bertrand,

Study hours

Scheduled activity hours
Lectures 20
Practical classes & workshops 10
Independent study hours
Independent study 120

Teaching staff

Staff member Role
Eduardo Fe Rodriguez Unit coordinator

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