Causal inference always goes beyond the data, even in randomized experiments, because experimental manipulations may affect other variables that caused the effect¶
Claim · All claims
Evidence · 1 study · theoreticalr3·q1
Currency · newest study 2023 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q1 i? Experimental manipulations may affect variables other than the treatment variable of interest, and those other variables may have caused the observed effect. → Grosz 2023
Evidence¶
Grosz 2023¶
Grosz, M. P. (2023). Should researchers make causal inferences and recommendations for practice on the basis of nonexperimental studies? Educational Psychology Review, 35, 57. https://doi.org/10.1007/s10648-023-09777-7
q1 · i? · theoretical · r3
Theoretical argument in the commentary: causal inference "always goes beyond the data, even in a randomized experiment". The author illustrates with a reading-program study where teacher adherence, not program content, might have caused comprehension differences.
"In a randomized experiment, the experimental manipulation might, for example, not only affect the treatment variable of interest but also other variables and these other variables (not the treatment variable of interest) might have caused the effect"
Discussion¶
Related Claims¶
- Adding multiple design elements to the basic ITS structure promotes causal inference by limiting alternative interpretations, demonstrated with No Child Left Behind — related
- Methods for estimating impacts when treatment effects vary apply to randomized controlled trials and are also applicable to quasi-experimental designs — related
- Confounding may explain the large retention effects reported for the Course Signals early warning system — related
- Nonexperimental designs, including natural experiments and structural equation modeling, can inform cause-effect relationships to a degree that depends on study design and analysis — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
causal-inference-goes-beyond-data-even-in-experiments |
title |
Causal inference always goes beyond the data, even in randomized experiments, because experimental manipulations may affect other variables that caused the effect |
description |
Causal inference always goes beyond the data, even in randomized experiments, because experimental manipulations may affect other variables that caused the effect |
status |
review |
evidence_strength |
weak |
newest_evidence |
2023 |
generated |
by process:wiki-ingest on 2026-10-21 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — grosz-2023 |
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