What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory

Lundberg, Ian; Johnson, Rebecca A.; Stewart, Brandon

doi:10.1177/00031224211004187

Cited by 201 publications

(176 citation statements)

References 84 publications

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“…However, in our opinion, the within-person effects of the RI-CLPM seem to be better suited for short-term studies of states that typically use shorter time lags (e.g., days) between assessments and are not concerned about systematic long-term changes (see also Orth et al, 2021). Interestingly, causal estimands that are defined at the within-person level seem to be less common in the causal inference literature (see Table 1 in Lundberg, Johnson, & Stewart, 2021, for a list of common causal estimands). Thus, it would be an important topic for future methodological work to integrate the withinperson perspective of the RI-CLPM into the potential outcome framework of causality (Usami, 2020(Usami, , 2021; see also Imai & Kim, 2019).…”

Section: Discussionmentioning

confidence: 96%

A Critique of the Random Intercept Cross-Lagged Panel Model

Lüdtke¹,

Robitzsch²

2021

Preprint

110

127

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The random intercept cross-lagged panel model (RI-CLPM) is an extension of the traditional cross-lagged panel model (CLPM) that allows controlling for stable trait factors when estimating cross-lagged effects. It has been argued that the RI-CLPM more appropriately accounts for trait-like, time-invariant stability of many psychological constructs and that it should be preferred over the CLPM when at least three waves of measurement are available. The basic idea of the RI-CLPM is to decompose longitudinal associations between two constructs into stable between-person associations and temporal within-person dynamics. The present article critically examines the RI-CLPM from a causal inference perspective. Using formal analysis and simulated data, we show that the RI-CLPM has limited potential to control for unobserved stable confounder variables when estimating cross-lagged effects. The CLPM with additional lag-2 effects sufficiently controls for delayed effects, as long as all relevant covariates are measured. Furthermore, we clarify that, in general, the RI-CLPM targets a different causal estimand than the CLPM. Whereas the cross-lagged effect in the CLPM targets the effect of increasing the exposure by one unit, the within-person cross-lagged effect in the RI-CLPM provides an estimate of the effect of increasing the exposure by one unit around the person mean. We argue that this within-person causal effect is typically less relevant for testing causal hypotheses with longitudinal data because it only captures temporary fluctuations around the individual person means and ignores the potential effects of causes that explain differences between persons.

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Section: Discussionmentioning

confidence: 96%

A Critique of the Random Intercept Cross-Lagged Panel Model

Lüdtke¹,

Robitzsch²

2021

Preprint

110

127

View full text Add to dashboard Cite

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“…It is generally not possible to answer many different questions with one design. Recently, similar points were made much more forcefully by Lundberg et al (2021). These authors argue that productive social research must start with precise definitions of the theoretical (research question) and empirical (parameter of interest) estimands.…”

Section: An Alternative First Step: Improving Social Research Practicementioning

confidence: 95%

“…In fact, in many published articles, there is often only a vague specification of the research question. The parameter of interest is not precisely defined and can be inferred only implicitly by the reader (see Lundberg et al 2021). There is no clear causal reasoning to justify model specification (Kohler et al 2018).…”

Section: Did the Csi Provide A Realistic Picture Of Social Science Research?mentioning

confidence: 99%

Has the Credibility of the Social Sciences Been Credibly Destroyed? Reanalyzing the “Many Analysts, One Data Set” Project

Auspurg

Brüderl

2021

Socius: Sociological Research for a Dynamic World

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In 2018, Silberzahn, Uhlmann, Nosek, and colleagues published an article in which 29 teams analyzed the same research question with the same data: Are soccer referees more likely to give red cards to players with dark skin tone than light skin tone? The results obtained by the teams differed extensively. Many concluded from this widely noted exercise that the social sciences are not rigorous enough to provide definitive answers. In this article, we investigate why results diverged so much. We argue that the main reason was an unclear research question: Teams differed in their interpretation of the research question and therefore used diverse research designs and model specifications. We show by reanalyzing the data that with a clear research question, a precise definition of the parameter of interest, and theory-guided causal reasoning, results vary only within a narrow range. The broad conclusion of our reanalysis is that social science research needs to be more precise in its “estimands” to become credible.

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“…For effects of hormonal contraceptives our theoretical estimand of interest (Lundberg et al, 2021) was the average treatment effect of hormonal contraception on sexual satisfaction, frequency, and so on. We strove to identify this causal effect by adjusting for confounding variables.…”

Section: Effects Of Hormonal Contraceptivesmentioning

confidence: 99%

Hormonal Contraception and Sexuality: Causal Effects, Unobserved Selection, or Reverse Causality?

Botzet

Gerlach

Driebe

et al. 2021

Collabra: Psychology

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Many of the women who take hormonal contraceptives discontinue because of unwanted side effects, including negative psychological effects. Yet scientific evidence of psychological effects is mixed, partly because causal claims are often based on correlational data. In correlational studies, possible causal effects can be difficult to separate from selection effects, attrition effects, and reverse causality. Contraceptive use and, according to the congruency hypothesis, congruent contraceptive use (whether a woman’s current use/non-use of a hormonal contraceptive is congruent with her use/non-use at the time of meeting her partner) have both been thought to influence relationship quality and sexual functioning. In order to address potential issues of observed and unobserved selection effects in correlational data, we studied a sample of up to 1,179 women to investigate potential effects of contraceptive use and congruent contraceptive use on several measures of relationship quality and sexual functioning: perceived partner attractiveness, relationship satisfaction, sexual satisfaction, and diary measurements including libido, frequency of vaginal intercourse, and frequency of masturbation. No evidence for substantial effects was found except for a positive effect of hormonal contraceptives on frequency of vaginal intercourse and a negative effect of hormonal contraceptives on frequency of masturbation. These effects were robust to the inclusion of observed confounders, and their sensitivity to unobserved confounders was estimated. No support for the congruency hypothesis was found. Our correlational study was able to disentangle, to some extent, causal effects of hormonal contraceptives from selection effects by estimating the sensitivity of reported effects. To reconcile experimental and observational evidence on hormonal contraceptives, future research should scrutinize the role of unobserved selection effects, attrition effects, and reverse causality.

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What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory

Cited by 201 publications

References 84 publications

A Critique of the Random Intercept Cross-Lagged Panel Model

A Critique of the Random Intercept Cross-Lagged Panel Model

Has the Credibility of the Social Sciences Been Credibly Destroyed? Reanalyzing the “Many Analysts, One Data Set” Project

Hormonal Contraception and Sexuality: Causal Effects, Unobserved Selection, or Reverse Causality?

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