Process Tracing and Contribution Analysis: A Combined Approach to Generative Causal Inference for Impact Evaluation

Abstract

This article proposes a combination of a popular evaluation approach, contribution analysis (CA), with an emerging method for causal inference, process tracing (PT). Both are grounded in generative causality and take a probabilistic approach to the interpretation of evidence. The combined approach is tested on the evaluation of the contribution of a teaching programme to the improvement of school performance of girls, and is shown to be preferable to either CA or PT alone. The proposed procedure shows that established Bayesian principles and PT tests, based on both science and common sense, can be applied to assess the strength of qualitative and quali-quantitative observations and evidence, collected within an overarching CA framework; thus shifting the focus of impact evaluation from ‘assessing impact’ to ‘assessing confidence’ (about impact).