At the heart of policy analysis is cause and effect: How can we determine or quantify the actual impact of an action or policy?
Without proper methods, recklessly inferring cause and effect from observed relationships can inform unhelpful policies at best, and dangerous ones at worst.
That’s where the Center for Causal Inference comes in. We innovate analytic approaches to yield estimates of causal relationships based on nonexperimental or observational data. And we recognize that even experimental data may require causal analysis.
Casual inference is involved in nearly every RAND research project, so it’s important that we get it right.
Our goals are to provide frameworks and estimation methods that reflect causality in the inferences we make, and to define what assumptions are necessary for those estimation methods to increase the certainty that we are drawing accurate conclusions.
Our Center develops, applies, and disseminates a toolbox of robust methodologies to ensure causal inferences are sound, and we offer training in those methods. We also provide seed funds to researchers to develop and improve upon causal inference methods—and in turn improve our ability to effectively analyze policy.
Our Focus
We apply methodological and statistical rigor to help our colleagues draw proper inferences. We encourage and support randomized studies, but when those aren’t feasible, we work with our colleagues to develop and use causal inference methods such as propensity scores, synthetic control, difference-in-difference, regression discontinuity, and instrumental variables, among others. We use these methods for estimations, and to determine the effectiveness of programs targeted at populations at risk or in need.
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