Statistics and causal inference.

Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81(396), 945-960.

Type

Journal article

Intellectual base

Primary research

Year

1986

DOI

10.2307/2289064

Link

https://doi.org/10.2307/2289064

Cited from

statistics_experimental_design

Cited by

4 citations across 4 artifacts.

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

Primes

• Experimental Design
  • Only one outcome is observed; the other is counterfactual (hypothetical), a formulation Holland (1986) crystallized as the "fundamental problem of causal inference".

    This sourcecrystallizes the "fundamental problem of causal inference" — only one potential outcome is observed per unit, so causation requires comparison across units made equivalent by design.

• Randomization
  • T1 — Randomization's inferential rigor versus feasibility constraints. Randomization provides the strongest causal-inference warrant but cannot always be implemented — ethical constraints (harmful interventions cannot be randomly assigned), practical constraints (cannot randomly assign macroeconomic conditions or cultural features), scientific constraints (units must be definable, assignment must be possible)

    This sourceCrystallizes the "fundamental problem of causal inference": only one potential outcome is observed per unit, so causation requires comparison across units made equivalent by design.

• Selection Bias

  • This sourceCrystallizes the "fundamental problem of causal inference": only one potential outcome is observed per unit, so causation requires comparison across units made equivalent by design.

Domain-specific

• Causal Inference
  • For each unit, outcomes under mutually exclusive treatment states cannot all be observed at once

Verification

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