A Unified Approach to Generalized Causal Inference
35 Pages Posted: 2 Aug 2013 Last revised: 29 Aug 2013
Date Written: August 1, 2013
Randomized controlled trials and natural experiments have been criticized for their lack of generalizability (external validity), questioning their usefulness to social science and policy. Here I show how three common approaches to generalizability -- the heuristic, statistical, and structural approaches -- are each incomplete on their own, and how generalized causal diagrams, or g-dags, can achieve a complete representation of the problem. G-dags combine theory and evidence to (1) make inferences from a study to a population or subgroup; (2) combine two or more studies that are not generalizable on their own into a generalized inference; (3) encode and test generalizable knowledge; and (4) provide a link to boosting algorithms as generalized additive models. More generally, g-dags make explicit what is being assumed, or questioned, in discussing the generalizability of experiments, which allows for constructive discourse and informed research agendas.
Keywords: external validity, generalizability, causal diagrams, dags, experiments, field experiments, program evaluation, social experiments
JEL Classification: C9
Suggested Citation: Suggested Citation