Causal Inference with Observational Data for Program Impact Evaluation
Causal inference techniques, when applied to observational (non-experimental) data, aim to determine cause-and-effect relationships rather than just correlations. This is crucial for nonprofits evaluating the true impact of their programs when randomized controlled trials are infeasible or unethical. Methods like propensity score matching, instrumental variables, or difference-in-differences allow nonprofits to estimate the 'counterfactual' outcome, i.e., what would have happened without the intervention, providing stronger evidence of program effectiveness.
It's like carefully untangling a knot of intertwined threads to see which one genuinely pulled something, when you couldn't just pull them one by one.
Provides rigorous evidence of a program's true impact without costly and often impractical experimental designs, improving accountability and funding pitches.
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