Our Focus: Causation, not Correlation

The Center for Causal Inference supports RAND researchers—and their clients—by applying methodological and statistical rigor to sometimes confounding questions. Here are some of our primary methods for inferring causality, with some examples and links.

Randomized Controlled Trials

Randomized controlled trials (RCTs) aim to reduce certain sources of bias when testing the effectiveness of a new treatment or policy action by randomly allocating subjects to two or more groups, treating them differently, and then comparing them with respect to a measured response.

RCTs may be the gold-standard methodology of determining causal effects, but they are rarely available when examining policy problems. And even when an RCT is available, it might not always demonstrate cause and effect or assess why something worked (or didn’t).

  • Female student talking to school counselor, photo by Monkey Business Images/Adobe Stock

    A Cluster-Randomized Trial of Restorative Practices

    The Study of Restorative Practices was a 5-year, cluster-randomized controlled trial (RCT) of the Restorative Practices Intervention in 14 middle schools in Maine to assess whether the intervention affects both positive developmental outcomes and problem behaviors; it was the first RCT of its kind.

  • Medical triage, illustrration by denyshutter/Adobe Stock

    Testing a Video Game Intervention to Recalibrate Physician Heuristics in Trauma Triage

    Researchers created a video game to recalibrate how trauma triage physicians determine whether a patient's injuries appear typical. They then conducted a randomized controlled trial to compare the effect of this game with that of another educational program on physicians' triage decisions.

More examples of Randomized Controlled Trials

Propensity Scores

Propensity scores help researchers balance the study groups and thus draw causal conclusions from observational studies. A propensity score is the probability that, based on certain characteristics, a study participant would be assigned to a specific treatment group. We help our RAND colleagues appropriately weigh, match, and apply propensity scores to their observational studies.

  • An example of Gaussian Process Regression (prediction) compared with other regression models, graph by Shiyu Ji/CC BY-SA 4.0

    Gaussian Process Framework Models Treatment Probability

    Propensity scores are commonly employed in observational study settings where the goal is to estimate average treatment effects. The paper introduces a flexible propensity score modelling approach, where the probability of treatment is modelled through a Gaussian process framework.

  • No Smoking Zone sign on a granite wall

    One Size Fits All? Disentangling the Effects of Tobacco Policies

    Among three state-level tobacco policies (cigarette taxation, tobacco control spending, and smoke-free air laws), a difference-in-differences analysis with generalized propensity scores found that only taxation significantly reduced smoking among the general adult population.

Synthetic Control

Synthetic control is similar to propensity scores in the sense that it helps researchers balance the study groups and thus draw causal conclusions from observational studies. It involves the construction of a weighted combination of groups used as controls, to which the treatment group is compared. This method is applicable even in the case of only one treatment observation, a scenario not covered by propensity score methods.

Matching Estimators

Matching estimators evaluate the effects of a treatment intervention by comparing outcomes for treated persons to those of similar persons in a comparison group. Treatment may represent, for example, participation in a training program, where the outcome is earnings or employment after the program intervention. Matching estimators are used with propensity score, synthetic control and other methods.

Instrumental Variables

Instrumental variables may also be used to estimate causal relationships when RCTs are not feasible. The instrumental variable approach for controlling unobserved sources of variability is the mirror opposite of the propensity score method for controlling observed variables. We help identify and use instrumental variables to control for confounding and measurement error in observational studies so we can make appropriate causal inferences.

  • Physician talking with female patient and giving medication, photo by Atstock Productions/Adobe Stock

    Instrumental Variable Methods for Effectiveness Research

    Small changes in analytic approach can yield contradictory results, which is demonstrated for antidepressant medication and counseling. With a sufficiently large sample size, instrumental variable estimation provides a possible solution and permits causal inferences under certain conditions.

  • A digital weather map

    The Not-So-Marginal Value of Weather Warning Systems

    Estimates of the benefits of weather warning systems are sparse, perhaps because there is often no clear counterfactual of how individuals would have fared without a particular warning system. Researchers used conditional variation in the initial broadcast dates of the National Oceanic and Atmospheric Administration's Weather Radio All Hazards (NWR) transmitters to produce both cross-sectional and fixed effects estimates of the causal impact of expanding the NWR transmitter network.

Difference-in-Differences

Difference-in-differences estimation is a statistical technique that is used after the fact to mimic an RCT using observational study data. In this case, we use longitudinal data from two groups to obtain an appropriate counterfactual to estimate a causal effect. We typically use this approach to estimate the effects of a specific intervention—such as passing a law, enacting a policy, or implementing a large-scale program—by comparing the changes in outcomes over time between a population that is affected by the intervention and a population that is not.

Regression-Discontinuity Design

Regression-discontinuity design allows us to determine whether a program is effective without requiring us to assign potentially vulnerable individuals to a "no-program" comparison group to evaluate the effectiveness of a program. In fact, we encourage the use of RDD when we wish to target a program or treatment to those who most need or deserve it—for example, students with low test scores, or patients in need of an experimental treatment.

  • Old european woman

    The Impact of Medical Insurance for the Poor in Georgia: A Regression Discontinuity Approach

    A rigorous program evaluation of the Medical Insurance Program for the Poor in the republic of Georgia looks at costs, usage and health behaviors under this system. The research design exploits the sharp discontinuities at two regional eligibility thresholds to estimate local average treatment effects.

  • Young girl petting a cat on a veranda

    No Link Found Between Pets and Kids' Health

    Contrary to popular belief, having a dog or cat in the home does not improve the mental or physical health of children. Researchers used a weighted propensity score regression approach and double robust regression analyses to examine the association between living with a dog or cat and health outcomes, while accounting for confounding factors.

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