Causal Inference for Development: Designs, Estimators & Judgement
From Potential Outcomes to Practitioner Practice
A design-based course in causal inference for people who design, read, and commission impact evidence. It starts with the potential-outcomes model, works through the estimator toolkit from randomisation to synthetic control and causal machine learning, and ends with the practitioner's real job: judging which design a claim can bear. Built on Imbens and Rubin, Angrist and Pischke, and Cunningham, with cases from Indian programmes.
R & Stata Code
Indian Cases
Interactive Lexicon
Why a Whole Course on Causal Inference?
Development practice runs on causal claims. A programme raised enrolment. A transfer cut stunting. A reform moved wages. Most of these claims are made from comparisons that cannot bear them. This course is about the gap between the comparison you can run and the one you actually need, and the designs that close it.
Where this sits. It goes one level past Econometrics 101, which introduces the methods. It carries the conceptual spine of the MEL flagship (counterfactual, attribution, contribution) into the estimators that operationalise it. If you have run a comparison and wondered whether it was honest, start here.
Identification before estimation
Every method is taught as an answer to one question: what has to be true for this comparison to recover a causal effect? The assumption comes first, the regression second.
Code you can run
R and Stata for each design, on data shapes you will actually meet: household surveys, programme rosters, district panels. Paired with the DevEconomics Toolkit's DiD, RDD, and synthetic control apps.
The buyer's seat
The final part is for the practitioner who commissions evidence rather than produces it: reading a results table, interrogating a design, and choosing what a claim can be made to bear.
"The most credible and influential research designs use random assignment… A constructive response to the credibility problem is to adopt designs that mimic randomised trials." — Joshua Angrist & Jörn-Steffen Pischke, Mostly Harmless Econometrics (2009), p. 11
Papers & Resources
A short, opinionated reading list. The textbooks anchor the theory; the rest are the field's reference points for each design.
Core textbooks
Imbens & Rubin, Causal Inference for Statistics, Social, and Biomedical Sciences (2015). Angrist & Pischke, Mostly Harmless Econometrics (2009). Cunningham, Causal Inference: The Mixtape (2021, free online). Morgan & Winship, Counterfactuals and Causal Inference (2nd ed., 2014).
Sibling courses
Pairs with The Evidence Question (MEL) for the conceptual spine and Econometrics 101 for the entry-level treatment. The DevEconomics Toolkit ships interactive DiD, RDD, and synthetic control apps used in the worked examples.
The credibility debate
Banerjee & Duflo, Poor Economics (2011), against Deaton, "Instruments, Randomization, and Learning about Development" (JEL 2010) and Pritchett & Sandefur on external validity (2015). Read all three before Module 12.