The DOI matches its Crossref record; 60 of the 62 references it cites were found in a registry or an index.
Statistics and Causal Inference
Abstract
Problems involving causal inference have dogged at the heels of statistics since its earliest days. Correlation does not imply causation, and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question is addressed by using a particular model for causal inference (Holland and Rubin 1983; Rubin 1974) to critique the discussions of other writers on causation and causal inference. These include selected philosophers, medical researchers, statisticians, econometricians, and proponents of causal modeling.
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In the citing paper
“As is usually the case, due to the fundamental problem of causal inference (Holland, 1986), we only observe one of the two potential outcomes for each unit i .”
From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Holland, 1986
In the citing paper
“causal effects can never be observed directly”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Holland 1986
In the citing paper
“In uplift modeling, however, the actual outcome that is estimated is unobserved, and therefore the error made by the model cannot be measured at the individual customer level.”
From Why you should stop predicting customer churn and start using uplift models (2021) · cites this paper as [34]
In the citing paper
“Historical data containing information on observed customer behavior never allows classifying customers in the four groups shown in Table 1, as the net e ↵ ect of treatment cannot be observed for an individual customer due to the fundamental problem of causal inference [34].”
From Why you should stop predicting customer churn and start using uplift models (2021) · cites this paper as [34]
In the citing paper
“Uplift or, in other words, whether a customer is persuadable, a lost cause, do-notdisturb or a sure thing, cannot be observed due to the fundamental problem of causal inference [34].”
From Why you should stop predicting customer churn and start using uplift models (2021) · cites this paper as [34]
In the citing paper
“Since the antecedent variable is an experimental variable in a randomized study, its causal relation to the mediator and the outcome variable can be interpreted unambiguously102,103.”
From Near transfer to an unrelated N-back task mediates the effect of N-back working memory training on matrix reasoning (2022) · cites this paper as [102]
In the citing paper
“Key to the ability to make inferences about mean causal effects are the assumptions of unit homogeneity and independence of units (Holland, 1986; King, Keohane and Verba, 1994: 91-97).”
From Multi-Method Research in the Social Sciences: A Review of Recent Frameworks and a Way Forward (2020) · cites this paper as Holland, 1986
In the citing paper
“When extant research finds the relationships between motivation and achievement, the interpretation with regards to causal relations remains difficult due to the lack of experimental manipulation (Granger, 1980;Holland, 1986;Marsh et al., 2018;Mega et al., 2014).”
From Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda (2022) · cites this paper as Holland, 1986
Citations
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Metadata
- Authors
- Holland, P. W.
- Affiliations
- Educational Testing Service (US)
- Corresponding author
- Holland, Paul W.
- ORCID
- Holland, Paul W.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1986-12
- Volume
- 81
- Issue
- 396
- Pages
- 945-960
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1978108654
- Cited by
- 5,293
- References
- 62
- Citations by year
- 2026: 188 · 2025: 260 · 2024: 246 · 2023: 266 · 2022: 274 · 2021: 323 · 2020: 302 · 2019: 269 · 2018: 259 · 2017: 202 · 2016: 212 · 2015: 229 · 2014: 271 · 2013: 224 · 2012: 187
- FWCI
- 14.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- causal inference, causal models, correlation versus causation, randomized experiments, counterfactual reasoning, philosophy of statistics
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