If intelligence is a cause, it is a within-subjects cause

Weinberger, Naftali

doi:10.1177/0959354315569832

Cited by 14 publications

(18 citation statements)

References 19 publications

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“…In this experiment there is no variation within the individual-they are either in the experimental or control group. The results, however, are direct estimates of within-subject treatment effects, despite the fact that they are between-subjects' estimates (Weinberger, 2015).…”

Section: Between-and Within-subjects Modelingmentioning

confidence: 78%

“…Via extrapolation from Simpson's paradox, there cannot be a cause within a population that is also not a cause within a subpopulation; the reason, such a statistical result may appear, is due to an improper formulation of covariation as causation, along with appropriate effect modifiers (Pearl, 2009). If we grant that individuals can count as subpopulations, it then follows that there can be no causal effect in a population that is also not a causal effect within at least one of its members (see Weinberger, 2015 for the elaboration of this argument). 3 For a concrete example, suppose we run an intervention where we randomly assign people to control or to working memory training groups.…”

Section: Between-and Within-subjects Modelingmentioning

confidence: 99%

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Effects of cognitive training on the structure of intelligence

Protzko

2016

Psychon Bull Rev

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Targeted cognitive training, such as n-back or speed of processing training, in the hopes of raising intelligence is of great theoretical and practical importance. The most important theoretical contribution, however, is not about the malleability of intelligence. Instead, I argue the most important and novel theoretical contribution is understanding the causal structure of intelligence. The structure of intelligence, most often taken as a hierarchical factor structure, necessarily prohibits transfer from subfactors back up to intelligence. If this is the true structure, targeted cognitive training interventions will fail to increase intelligence not because intelligence is immutable, but simply because there is no causal connection between, say, working memory and intelligence. Seeing the structure of intelligence for what it is, a causal measurement model, allows us to focus testing on the presence and absence of causal links. If we can increase subfactors without transfer to other facets, we may be confirming the correct causal structure more than testing malleability. Such a blending into experimental psychometrics is a strong theoretical pursuit.

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Section: Between-and Within-subjects Modelingmentioning

confidence: 78%

Section: Between-and Within-subjects Modelingmentioning

confidence: 99%

Effects of cognitive training on the structure of intelligence

Protzko

2016

Psychon Bull Rev

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“…This is why, more generally, the question of whether an effect averages over heterogeneous populations is orthogonal to whether it is a genuine effect. To the extent that we can measure average effects in heterogeneous populations, it is because this effect averages over the total effects for the individuals in that population (Weinberger, 2015). What matters are an individual's outcomes for counterfactual values of the treatment, rather than whether there is actual variation among individuals.…”

Section: Path-specific Effects and Probabilistic Causalitymentioning

confidence: 99%

Path-Specific Effects

Weinberger

2019

The British Journal for the Philosophy of Science

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A cause may influence its effect via multiple paths. Paradigmatically (Hesslow, 1974), taking birth control pills both decreases one's risk of thrombosis by preventing pregnancy and increases it by producing a blood chemical. Building on Pearl (2001), I explicate the notion of a path-specific effect. Roughly, a path-specific effect of C on E via path P is the degree to which a change in C would change E were they to be transmitted only via P. Facts about such effects may be gleaned from the structural equations commonly used to represent the causal relationships among variables. I contrast my analysis of the Hesslow case with those given by theorists of probabilistic causality, who mistakenly link it to issues of causal heterogeneity, token-causation and indeterminism. The reason probabilistic theories misdiagnose this case is that they pay inadequate attention to the structural relationships among variables.

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“…We need not adopt a realist or an instrumentalist view on latent variables to appreciate the point that theoretical criteria, formulated in terms of such latents, can be replaced by causal assumptions and intervention data. Similarly, the insightful discussion in Weinberger ( 2015 ) on latents and ideal interventions is relevant but not crucial: our points do not hinge on the interventions on latents being ideal.…”

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confidence: 90%

Intervention and Identifiability in Latent Variable Modelling

Romeijn¹,

Williamson²

2018

Minds & Machines

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We consider the use of interventions for resolving a problem of unidentified statistical models. The leading examples are from latent variable modelling, an influential statistical tool in the social sciences. We first explain the problem of statistical identifiability and contrast it with the identifiability of causal models. We then draw a parallel between the latent variable models and Bayesian networks with hidden nodes. This allows us to clarify the use of interventions for dealing with unidentified statistical models. We end by discussing the philosophical and methodological import of our result.

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If intelligence is a cause, it is a within-subjects cause

Cited by 14 publications

References 19 publications

Effects of cognitive training on the structure of intelligence

Effects of cognitive training on the structure of intelligence

Path-Specific Effects

Intervention and Identifiability in Latent Variable Modelling

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