Causal Inference with Observational Data for Program Impact Evaluation

Causal inference methods, such as propensity score matching, instrumental variables, or difference-in-differences, allow nonprofits to estimate the true causal impact of their programs when randomized controlled trials (RCTs) are not feasible. By carefully constructing control groups from observational data that are comparable on key characteristics, these techniques help isolate the effect of the intervention itself, rather than simply observing correlations. This is crucial for understanding what truly drives change in complex social systems.