Cited by 574Statistical Methods and Bayesian InferencePsychometric Methodologies and TestingBayesian Modeling and Causal Inference

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Taking Account of Time Lags in Causal Models

Gollob, Harry F., Reichardt, Charles S.·1987·Child Development

DOI 10.2307/1130293

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Abstract

Although it takes time for a cause to exert an effect, causal models often fail to allow adequately for time lags. In particular, causal models that contain cross-sectional relations (i.e., relations between values of 2 variables at the same time) are unsatisfactory because they omit the values of variables at prior times, they omit effects that variables can have on themselves, and they fail to specify the length of the causal interval that is being studied. These omissions can produce severe biases in estimates of the size of causal effects. Longitudinal models also can fail to take account of time lags properly, and this too can lead to severely biased estimates. The discussion illustrates the biases that can occur in both cross-sectional and longitudinal models, introduces the latent longitudinal approach to causal modeling, and shows how latent longitudinal models can be used to reduce bias by taking account of time lags even when data are available for only 1 point in time.

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Authors: Gollob, H. F., & Reichardt, C. S.
Affiliations: University of Denver (US)
ORCID: Reichardt, Charles S.
Venue: Child Development
Publisher: Oxford University Press (OUP)
Published: 1987-02
Volume: 58
Issue: 1
Pages: 80
ISSN: 0009-3920 (ISSN-L)
DOI: 10.2307/1130293
OpenAlex: W2048878602
Cited by: 572
References: 21
Citations by year: 2026: 10 · 2025: 23 · 2024: 34 · 2023: 34 · 2022: 37 · 2021: 28 · 2020: 22 · 2019: 24 · 2018: 32 · 2017: 23 · 2016: 18 · 2015: 22 · 2014: 19 · 2013: 9 · 2012: 10
FWCI: 11.11
Type: journal article
Language: en
Open access: closed
Locations: Child Development (publishedVersion)
PubMed (publishedVersion)
Funders: National Institute of Mental Health (MH38305)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords: time delay, causal models, longitudinal modeling, estimation bias
MeSH terms: Child, Child Development, Female, Humans, Intelligence, Longitudinal Studies, Models, Psychological, Mothers, Statistics as Topic, Time Factors

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Taking Account of Time Lags in Causal Models

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