@article{Averell_2011,
	doi = {10.1016/j.jmp.2010.08.009},
	url = {https://doi.org/10.1016%2Fj.jmp.2010.08.009},
	year = 2011,
	month = {feb},
	publisher = {Elsevier {BV}},
	volume = {55},
	number = {1},
	pages = {25--35},
	author = {Lee Averell and Andrew Heathcote},
	title = {The form of the forgetting curve and the fate of memories},
	journal = {Journal of Mathematical Psychology}
}


@article{chatton_causal_2024,
	title = {The {Causal} {Cookbook}: {Recipes} for {Propensity} {Scores}, {G}-{Computation}, and {Doubly} {Robust} {Standardization}},
	volume = {7},
	issn = {2515-2459},
	shorttitle = {The {Causal} {Cookbook}},
	url = {https://doi.org/10.1177/25152459241236149},
	doi = {10.1177/25152459241236149},
	language = {en},
	number = {1},
	urldate = {2025-02-22},
	journal = {Advances in Methods and Practices in Psychological Science},
	author = {Chatton, Arthur and Rohrer, Julia M.},
	month = jan,
	year = {2024},
	note = {Publisher: SAGE Publications Inc},
	pages = {25152459241236149}
}


@article{gabriel_inverse_2024,
	title = {Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation},
	volume = {43},
	issn = {0277-6715, 1097-0258},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.9969},
	doi = {10.1002/sim.9969},
	language = {en},
	number = {3},
	urldate = {2025-02-22},
	journal = {Statistics in Medicine},
	author = {Gabriel, Erin E. and Sachs, Michael C. and Martinussen, Torben and Waernbaum, Ingeborg and Goetghebeur, Els and Vansteelandt, Stijn and Sjölander, Arvid},
	month = feb,
	year = {2024},
	pages = {534--547}
}


@article{dickerman_counterfactual_2020,
	title = {Counterfactual prediction is not only for causal inference},
	volume = {35},
	issn = {0393-2990, 1573-7284},
	url = {https://link.springer.com/10.1007/s10654-020-00659-8},
	doi = {10.1007/s10654-020-00659-8},
	language = {en},
	number = {7},
	urldate = {2025-02-22},
	journal = {European Journal of Epidemiology},
	author = {Dickerman, Barbra A. and Hernán, Miguel A.},
	month = jul,
	year = {2020},
	pages = {615--617},
}


@misc{ho_matchit_2005,
	title = {{MatchIt}: {Nonparametric} {Preprocessing} for {Parametric} {Causal} {Inference}},
	shorttitle = {{MatchIt}},
	url = {https://CRAN.R-project.org/package=MatchIt},
	doi = {10.32614/CRAN.package.MatchIt},
	language = {en},
	urldate = {2025-02-23},
	author = {Ho, Daniel and Imai, Kosuke and King, Gary and Stuart, Elizabeth and Greifer, Noah},
	month = jan,
	year = {2005},
	note = {Institution: Comprehensive R Archive Network}
}


@article{kostouraki_variance_2024,
	title = {On variance estimation of the inverse probability‐of‐treatment weighting estimator: {A} tutorial for different types of propensity score weights},
	volume = {43},
	issn = {0277-6715, 1097-0258},
	shorttitle = {On variance estimation of the inverse probability‐of‐treatment weighting estimator},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.10078},
	doi = {10.1002/sim.10078},
	language = {en},
	number = {13},
	urldate = {2025-02-25},
	journal = {Statistics in Medicine},
	author = {Kostouraki, Andriana and Hajage, David and Rachet, Bernard and Williamson, Elizabeth J. and Chauvet, Guillaume and Belot, Aurélien and Leyrat, Clémence},
	month = jun,
	year = {2024},
	pages = {2672--2694}
}


@article{reifeis_variance_2022,
	title = {On {Variance} of the {Treatment} {Effect} in the {Treated} {When} {Estimated} by {Inverse} {Probability} {Weighting}},
	volume = {191},
	copyright = {https://academic.oup.com/journals/pages/open\_access/funder\_policies/chorus/standard\_publication\_model},
	issn = {0002-9262, 1476-6256},
	url = {https://academic.oup.com/aje/article/191/6/1092/6519755},
	doi = {10.1093/aje/kwac014},
	language = {en},
	number = {6},
	urldate = {2025-02-25},
	journal = {American Journal of Epidemiology},
	author = {Reifeis, Sarah A and Hudgens, Michael G},
	month = may,
	year = {2022},
	pages = {1092--1097}
}


@article{li_weighting_2013,
	title = {A {Weighting} {Analogue} to {Pair} {Matching} in {Propensity} {Score} {Analysis}},
	volume = {9},
	issn = {1557-4679, 2194-573X},
	url = {https://www.degruyter.com/document/doi/10.1515/ijb-2012-0030/html},
	doi = {10.1515/ijb-2012-0030},
	number = {2},
	urldate = {2025-03-03},
	journal = {The International Journal of Biostatistics},
	author = {Li, Liang and Greene, Tom},
	month = jan,
	year = {2013}
}


@misc{heiss_marginal_2022,
	title = {Marginal and conditional effects for {GLMMs} with \{marginaleffects\}},
	copyright = {https://creativecommons.org/licenses/by/4.0/legalcode},
	url = {https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects},
	abstract = {As a field, statistics is really bad at naming things. Take, for instance, the term “fixed effects.” In econometrics and other social science-flavored statistics, this typically refers to categorical terms in a regression model.},
	language = {en},
	urldate = {2025-04-01},
	journal = {Andrew Heiss's blog},
	author = {Heiss, Andrew},
	month = nov,
	year = {2022},
	doi = {10.59350/xwnfm-x1827},
}


@Manual{russell2024emmeans,
  title = {{emmeans}: Estimated Marginal Means, aka Least-Squares Means},
  author = {Russell V. Lenth},
  year = {2024},
  note = {R package version 1.10.4},
  doi = {10.32614/CRAN.package.emmeans},
  url = {https://doi.org/10.32614/CRAN.package.emmeans}
}


@article{arel2024interpret,
  title={How to interpret statistical models using marginaleffects for {R} and {Python}},
  author={Arel-Bundock, Vincent and Greifer, Noah and Heiss, Andrew},
  journal={Journal of Statistical Software},
  volume={111},
  pages={1-32},
  year={2024},
  doi = {10.18637/jss.v111.i09},
  url = {https://doi.org/10.18637/jss.v111.i09}
}


@article{ludecke2019insight,
  title={{insight}: A unified interface to access information from model objects in R},
  author={L{\"u}decke, Daniel and Waggoner, Philip D and Makowski, Dominique},
  journal={Journal of Open Source Software},
  volume={4},
  number={38},
  pages={1412},
  year={2019},
  doi = {10.21105/joss.01412},
  url = {https://doi.org/10.21105/joss.01412}
}


@article{dong_identifying_2023,
	title = {Identifying and {Estimating} {Causal} {Moderation} for {Treated} and {Targeted} {Subgroups}},
	volume = {58},
	issn = {0027-3171, 1532-7906},
	url = {https://www.tandfonline.com/doi/full/10.1080/00273171.2022.2046997},
	doi = {10.1080/00273171.2022.2046997},
	language = {en},
	number = {2},
	urldate = {2025-08-28},
	journal = {Multivariate Behavioral Research},
	author = {Dong, Nianbo and Kelcey, Benjamin and Spybrook, Jessaca},
	month = mar,
	year = {2023},
	pages = {221--240},
}


@article{bell_understanding_2018,
	title = {Understanding and misunderstanding group mean centering: a commentary on {Kelley} et al.’s dangerous practice},
	volume = {52},
	issn = {0033-5177, 1573-7845},
	shorttitle = {Understanding and misunderstanding group mean centering},
	url = {http://link.springer.com/10.1007/s11135-017-0593-5},
	doi = {10.1007/s11135-017-0593-5},
	language = {en},
	number = {5},
	urldate = {2018-08-09},
	journal = {Quality \& Quantity},
	author = {Bell, Andrew and Jones, Kelvyn and Fairbrother, Malcolm},
	month = sep,
	year = {2018},
	pages = {2031--2036},
}


@article{rohrer_these_2023,
	title = {These {Are} {Not} the {Effects} {You} {Are} {Looking} for: {Causality} and the {Within}-/{Between}-{Persons} {Distinction} in {Longitudinal} {Data} {Analysis}},
	volume = {6},
	issn = {2515-2459},
	shorttitle = {These {Are} {Not} the {Effects} {You} {Are} {Looking} for},
	url = {https://doi.org/10.1177/25152459221140842},
	doi = {10.1177/25152459221140842},
	abstract = {In psychological science, researchers often pay particular attention to the distinction between within- and between-persons relationships in longitudinal data analysis. Here, we aim to clarify the relationship between the within- and between-persons distinction and causal inference and show that the distinction is informative but does not play a decisive role in causal inference. Our main points are threefold. First, within-persons data are not necessary for causal inference; for example, between-persons experiments can inform about (average) causal effects. Second, within-persons data are not sufficient for causal inference; for example, time-varying confounders can lead to spurious within-persons associations. Finally, despite not being sufficient, within-persons data can be tremendously helpful for causal inference. We provide pointers to help readers navigate the more technical literature on longitudinal models and conclude with a call for more conceptual clarity: Instead of letting statistical models dictate which substantive questions researchers ask, researchers should start with well-defined theoretical estimands, which in turn determine both study design and data analysis.},
	language = {EN},
	number = {1},
	urldate = {2026-04-26},
	journal = {Advances in Methods and Practices in Psychological Science},
	publisher = {SAGE Publications Inc},
	author = {Rohrer, Julia M. and Murayama, Kou},
	month = jan,
	year = {2023},
	pages = {25152459221140842},
}
