Construct · 1 fields · 3 citations

propensity score: a standalone construct

One name. One construct. A landmark on the map.

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propensityscore1 CONSTRUCTPropensity scoreR&R · BALANCE SCORE

A single standalone construct; nearby constructs are located against it in the Collider. Tap a field for its definition, unit, and measures.

Statistics (causal inference)

The probability of receiving treatment given observed covariates

Paul Rosenbaum and Donald Rubin introduced the propensity score in 1983 as the conditional probability that a unit receives the treatment rather than the control condition, given its observed background characteristics (its covariates). In an observational study, where treatment is not assigned at random, the treated and untreated groups usually differ systematically on these covariates, and Rosenbaum and Rubin proved that the propensity score is a balancing score: among units that share the same score, the observed covariates have the same distribution in the treated and control groups, so a comparison across those units estimates the treatment effect without bias from the measured covariates. Their 1984 paper showed how to use the score in practice by sorting units into strata of similar scores, and later work matches, weights, or adjusts on it. As a landmark, the propensity score reduces a large set of covariates to a single number that summarises the whole process by which units come to be treated, and it accounts only for the covariates that are observed and included, leaving any unmeasured differences uncorrected.[1][2][3]

Unit
A probability between 0 and 1 assigned to each unit, the estimated chance of receiving treatment given its observed covariates
Recovery means
Not applicable
Measured by
Usually estimated by regressing the binary treatment indicator on observed covariates (commonly logistic regression, or machine-learning classifiers), and then used to match, stratify, or weight units

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Nearby constructs

score2 fields · 3 citationsjingle · one word, two constructsmatching4 fields · 4 citationsjingle · one word, four constructsbias6 fields · 8 citationsjingle · one name, six constructs

References

  1. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41
  2. Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 516–524. https://doi.org/10.1080/01621459.1984.10478078
  3. Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424. https://doi.org/10.1080/00273171.2011.568786

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