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Causal Inference for Development: Designs, Estimators & Judgement

Reading list

Every reading this course assigns, module by module, with the best free copy we could find.

33 readings22 free to read10 on request1 book

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Module 1. Module 1: The Counterfactual Problem

  1. Hernán, M. A. and Robins, J. M. (2020). Causal Inference: What If, ch. 1. Chapman and Hall/CRC.Read it free
  2. Holland, P. W. (1986). Statistics and Causal Inference. Journal of the American Statistical Association, 81(396), 945-960.Publisher pageSubscription
  3. Angrist, J. D. and Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion, ch. 2. Princeton University Press.Publisher pageBook
  4. Imbert, C. and Papp, J. (2015). Labor Market Effects of Social Programs: Evidence from India's Employment Guarantee. American Economic Journal: Applied Economics, 7(2), 233-263.Publisher pageSubscription

Module 2. Module 2: Selection Bias and the Identifying Assumption

  1. Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Ch. 5, Matching and Subclassification (ch. 5, Unconfoundedness, in the online edition). Free online edition on the author's site, now revised as The Remix.Read it free
  2. Rosenbaum, P. R. and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.Publisher pageSubscription

Module 3. Module 3: Causal Graphs: Confounders, Colliders, and Bad Controls

  1. Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Ch. 3, Directed Acyclic Graphs. Free online edition on the author's site, now revised as The Remix.Read it free
  2. Cinelli, C., Forney, A. and Pearl, J. (2024). A Crash Course in Good and Bad Controls. Sociological Methods and Research, 53(3), 1071-1104.Read it free
  3. Hernán, M. A. and Robins, J. M. (2020). Causal Inference: What If, chs. 6-7. Chapman and Hall/CRC.Read it free

Module 4. Module 4: Randomisation and the RCT

  1. Abdul Latif Jameel Poverty Action Lab (n.d.). Introduction to Randomized Evaluations. J-PAL.Read it free
  2. Banerjee, A. V., Duflo, E., Glennerster, R. and Kothari, D. (2010). Improving immunisation coverage in rural India: clustered randomised controlled evaluation of immunisation campaigns with and without incentives. BMJ, 340, c2220.Read it free

Module 5. Module 5: Matching and Propensity Scores

  1. Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Ch. 5, Matching and Subclassification (ch. 5, Unconfoundedness, in the online edition). Free online edition on the author's site, now revised as The Remix.Read it free
  2. Rosenbaum, P. R. and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.Publisher pageSubscription
  3. Stuart, E. A. (2010). Matching Methods for Causal Inference: A Review and a Look Forward. Statistical Science, 25(1), 1-21.Free copy, a working-paper versionPublisher page

Module 6. Module 6: Instrumental Variables

  1. Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Ch. 7, Instrumental Variables. Free online edition on the author's site, now revised as The Remix.Read it free
  2. Imbens, G. W. and Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.Publisher pageSubscription
  3. Duflo, E. and Pande, R. (2007). Dams. Quarterly Journal of Economics, 122(2), 601-646.Publisher pageSubscription

Module 7. Module 7: Regression Discontinuity

  1. Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Ch. 6, Regression Discontinuity. Free online edition on the author's site, now revised as The Remix.Read it free
  2. Lee, D. S. and Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.Publisher pageSubscription
  3. Cattaneo, M. D., Idrobo, N. and Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge Elements: Quantitative and Computational Methods for Social Science. Cambridge University Press.Read it free

Module 8. Module 8: Difference-in-Differences

  1. Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Ch. 9, Difference-in-Differences (ch. 8a in the online edition). Free online edition on the author's site, now revised as The Remix.Read it free
  2. Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254-277.Publisher pageSubscription
  3. Callaway, B. and Sant'Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.Free copy, a working-paper versionPublisher page

Module 9. Module 9: Synthetic Control

  1. Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Ch. 10, Synthetic Control (ch. 9 in the online edition). Free online edition on the author's site, now revised as The Remix.Read it free
  2. Abadie, A. (2021). Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), 391-425.Publisher pageSubscription
  3. Abadie, A., Diamond, A. and Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.Read it free

Module 10. Module 10: Causal Machine Learning

  1. Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. and Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. Econometrics Journal, 21(1), C1-C68.Read it free
  2. Athey, S., Tibshirani, J. and Wager, S. (2019). Generalized random forests. Annals of Statistics, 47(2), 1148-1178.Read it free

Module 11. Module 11: Reading and Interrogating an Evaluation

  1. Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B. and Vermeersch, C. M. J. (2016). Impact Evaluation in Practice, 2nd edition, ch. 3, Causal Inference and Counterfactuals. Inter-American Development Bank and World Bank.Read it free
  2. Deaton, A. and Cartwright, N. (2018). Understanding and misunderstanding randomized controlled trials. Social Science and Medicine, 210, 2-21.Free copy of the published versionPublisher page

Module 12. Module 12: When There Is No Counterfactual

  1. White, H. and Phillips, D. (2012). Addressing Attribution of Cause and Effect in Small n Impact Evaluations: Towards an Integrated Framework. 3ie Working Paper 15. International Initiative for Impact Evaluation.Read it free
  2. Mayne, J. (2012). Contribution analysis: Coming of age? Evaluation, 18(3), 270-280.Publisher pageSubscription

Module 13. Module 13: Capstone Project

  1. Abdul Latif Jameel Poverty Action Lab (n.d.). The Elements of a Randomized Evaluation. J-PAL.Read it free

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