The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Taking Account of Time Lags in Causal Models
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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“As the outcomes of these longitudinal models depend largely on the time-interval between observations41,42, the annual nature of the data needs to be considered.”
From Windows of developmental sensitivity to social media (2022) · cites this paper as [41]
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Practical issues in structural modeling
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Diary Methods: Capturing Life as it is Lived
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Metadata
- Authors
- Gollob, H. F., & Reichardt, C. S.
- Affiliations
- University of Denver (US)
- Venue
- Child Development
- Publisher
- Oxford University Press (OUP)
- Published
- 1987-02
- Volume
- 58
- Issue
- 1
- Pages
- 80
- ISSN
- 0009-3920 (ISSN-L)
- 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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