Evaluation, Ahead of Print.
We describe a simple yet rigorous graphical method for eliminating bias in theory-based program evaluation. The method is an application to social and international development program evaluation of the graphical causal models used to test medical treatments. We implement a graphical causal model for the World Bank’s well-known Bangladesh Integrated Nutrition Project. We show how to construct the graphical causal model to represent program theory in context in explicitly causal terms. We then show how to visually inspect the graphical causal model to distinguish causal from non-causal associations between variables in evaluation data. Finally, we show how to select a set of adjustment variables to neutralize non-causal associations, eliminating bias in all forms of causal inference—qualitative and quantitative, linear and non-linear.