Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.

Rubin, D. B. (1974). Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66(5), 688-701.

Type

Journal article

Intellectual base

Primary research

Year

1974

DOI

10.1037/h0037350

Link

https://doi.org/10.1037/h0037350

Cited from

military_security, philosophy, statistics_experimental_design

Cited by

8 citations across 8 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Blocking (In Experimental Design)
  • T3 — Design-based control versus model-based control. Blocking achieves variance reduction through how units are assigned; covariate adjustment in regression achieves it through how the model is fit

    This sourceFoundational potential-outcomes framework defining causal effects as comparisons under hypothetical treatments holding background conditions fixed, contrasting design-based and model-based (covariate-adjustment) control

• Counterfactual Subtraction
  • The reasoning generalises to any decomposition of an observed quantity into "what happened" minus "what would have happened," which is why the prime sits one level below causality as a general method of operationalising a causal claim into a number.

    This sourceFoundational potential-outcomes framework defining a causal effect as the difference between observed and counterfactual potential outcomes, with randomisation/exchangeability as the identifying assumption.

• Counterfactuals
  • A statement like "if I had studied harder, I would have passed"

    This sourceFoundational potential-outcomes framework: causal effects as comparisons of Y(1) and Y(0) under hypothetical treatments holding background fixed; underlies the fundamental problem of causal inference (only one potential outcome observed per unit). Verified support.

• Experimental Design
  • Experimental design makes this counterfactual reasoning concrete by randomization or matched design, formalized in Rubin's (1974) potential-outcomes framework.

    This sourcefoundational potential-outcomes framework defining causal effects as comparisons of outcomes under hypothetical treatments holding background conditions fixed, making counterfactual reasoning concrete via randomization/matched design.

• Factorial Design
  • T4 — Controlled-factor clarity versus observational realism. Factorial designs require manipulating each factor to pre-specified levels, which maximizes causal clarity but sometimes requires artificial or unrealistic factor combinations

    This sourcepotential-outcomes framework defining causal effects via comparisons under hypothetical treatments holding background fixed; grounds the controlled-manipulation vs observational-realism tension (factorial designs require manipulating factors to pre-specified levels for causal clarity).

• Minimal Modification Principle
  • A randomized controlled trial applies minimal modification implicitly: randomization holds background variables constant while varying treatment, a logic Rubin (1974) formalized through the potential-outcomes framework for causal inference.

    This sourceFoundational potential-outcomes framework: defines causal effects as comparisons of outcomes under hypothetical treatments holding background conditions fixed; formalizes minimal modification implicit in randomized controlled trials and observational designs.

• Preparatory Field Conditioning
  • If the focal act would have gone the same way on untouched ground, the upstream work was something else, whatever the schedule called it.

    This sourceDefines the effect of a treatment as a comparison between the outcome under treatment and the outcome the same unit would have had untreated, making the untouched condition the reference against which any act is measured.

• Randomization
  • 6. The credibility-restoration property in causal claims — randomization is the unique procedure that, if properly implemented and preserved through analysis, permits credible causal attribution of observed differences to treatment effects rather than to pre-existing selection bias, rendering even single-arm observational associations interpretable as causal within the randomized population

    This sourceFoundational potential-outcomes framework: defines causal effects as comparisons of outcomes under hypothetical treatments holding background conditions fixed; formalizes minimal modification implicit in randomized controlled trials and observational designs.

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