[Causality an causal-inference in epidemiologic study].
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Disease↗
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[Causality or mere statistical association. Criteria for causal evaluations].
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Epidemiologic Methods↗
Structure and strength in causal induction.
We present a framework for the rational analysis of elemental causal induction-learning about the existence of a relationship between a single cause and effect-based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, DeltaP and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between DeltaP and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either DeltaP or causal power.
Adult↗
Causal reasoning and the diagnostic process.
BACKGROUND: Causal reasoning as a way to make a diagnosis seems convincing. Modern medicine depends on the search for causes of disease and it seems fair to assert that such knowledge is employed in diagnosis. Causal reasoning as it has been presented neglects to some extent the conception of multifactorial disease causes. GOAL: The purpose of this paper is to analyze aspects of causation relevant for discussing causal reasoning in a diagnostic context. PROCEDURES: The analysis will discuss different conceptions of causal reasoning in medical diagnosis, discriminating primarily between narrow causal diagnosis and more thorough causal explanation. The theory of causes as non-redundant factors in effective causal complexes is used as an analytical background. Causal explanations are performed according to different causal models. Such models of diagnosis are assumptions concerning structure and mechanisms, which cannot be directly or immediately observed. Conceptions and results of causal search strategies differ, according to the focus of the searcher. Causal reasoning is also seen in diagnosis in a more extensive meaning: the pin-pointing of factors responsible for the condition of the patient at any time during the course of disease. CONCLUSION: Causal reasoning and diagnosis go well in hand, especially if both concepts are widened. The theory of causes as non-redundant components in effective causal complexes, modulated by what is referred to as the stop problem and causal fields, is valuable for explaining the many aspects of causal reasoning in medical diagnosis.
Causality↗
Causal models in primary open angle glaucoma.
OBJECTIVES: To apply three of the most common epidemiologic causal models for glaucoma and explain the issues pertaining to the use of them. METHODS: Three causal models will be explained: (1) counterfactual model, (2) graphical model, and (3) sufficient-component causal model. Using literature search and experts' opinions these models will be applied in glaucoma. RESULTS: Introducing the counterfactual model, as one of the most important quantitative causal models and formalizes the concepts of cause and effect, this article emphasizes the limitations with the use of this model for glaucoma. Explaining causal diagram, as a useful qualitative tool that provides a lucid depiction of the assumptions concerning a causal pathway, this article provides a working causal diagram for glaucoma based on the evidence in the literature and experts' comments. In addition, the limitations in developing appropriate causal diagrams for glaucoma are discussed. Finally, the sufficient-component causal models and their importance in portraying the mechanics of causal interactions will be discussed. CONCLUSION: Paucity of evidence is the main issue with the use of causal models for glaucoma. This article calls for convergent review of different theories and different pieces of evidence that can enable us to apply different causal models for glaucoma.
Causality↗
Perception and judgement of physical causality involve different brain structures.
One basic type of 'mechanical' causality is that which occurs between physical objects. Subjects were presented with mechanically causal events (ball collides with and causes movement of another ball) or two non-causal events (a ball either passes underneath another ball, or rolls across the screen and changes colour). We investigated which brain regions exhibit increased activity during the judgement of causality ('judged causality') as compared with judgement of movement direction ('perceived causality'). We show an increase of medial frontal cortex activity when subjects were explicitly instructed to search for causality. Moreover, this increase was specifically associated with the search for causality and not with the perception of causality because the signal increase occurs whatever the nature of the stimulus (causal or non causal). Our study provides evidence for brain regions involved in a conscious level of inference about the presence of causality.
Adult↗
Causal connectivity of evolved neural networks during behavior.
To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics.
Aging↗
Transcriptome-Wide Root Causal Inference.
Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously recovers a causal ordering of the expression levels to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.
Journal Article↗
Predictive versus diagnostic causal learning: evidence from an overshadowing paradigm.
Causal directionality belongs to one of the most fundamental aspects of causality that cannot be reduced to mere covariation. This paper is part of a debate between proponents of associative theories, which claim that learners are insensitive to the causal status of cues and outcomes, and proponents of causal-model theory, which postulates an interaction of assumptions about causal directionality and learning. Some researchers endorsing the associationist view have argued that evidence for the interaction between cue competition and causal directionality may be restricted to two-phase blocking designs. Furthermore, from the viewpoint of causal-model theory, blocking designs carry the potential problem that the predicted asymmetries of cue competition are partly dependent on asymmetries of retrospective inferences. The present experiments use a one-phase overshadowing paradigm that does not allow for retrospective inferences and therefore represents a more unambiguous test of sensitivity to causal directionality. The results strengthen causal-model theory by clearly demonstrating the influence of causal directionality on learning. However, they also provide evidence for boundary conditions for this effect by highlighting the role of the semantics of the learning task.
Adult↗
A study in causal discovery from population-based infant birth and death records.
In the domain of medicine, identification of the causal factors of diseases and outcomes, helps us formulate better management, prevention and control strategies for the improvement of health care. With the goal of exploring, evaluating and refining techniques to learn causal relationships from observational data, such as data routinely collected in healthcare settings, we focused on investigating factors that may contribute causally to infant mortality in the United States. We used the U.S. Linked Birth/Infant Death dataset for 1991 with more than four million records and about 200 variables for each record. Our sample consisted of 41,155 records randomly selected from the whole dataset. Each record had maternal, paternal and child factors and the outcome at the end of the first year--whether the infant survived or not. For causal discovery we used a modified Local Causal Discovery (LCD2) algorithm, which uses the framework of causal Bayesian Networks to represent causal relationships among model variables. LCD2 takes as input a dataset and outputs causes of the form variable X causes variable Y. Using the infant birth and death dataset as input, LCD2 output nine purported causal relationships. Eight out of the nine relationships seem plausible. Even though we have not yet discovered a clinically novel causal link, we plan to look for novel causal pathways using the full sample after refining the algorithm and developing a more efficient implementation.
Algorithms↗
Perceived physical and social causality in animated motions: spontaneous reports and ratings.
Michotte argued that we perceive cause-and-effect, without contributions from reasoning or learning, even in displays of two-dimensional moving shapes. Two studies extend this line of work from perception of mechanical to social causality. We compared verbal reports with structured ratings of causality to gain a better understanding of the extent to which perceptual causality occurs spontaneously or depends on instruction or context. A total of 120 adult observers (72 in the main experiment, 48 in an initial experiment) saw 12 (or 8) different computer animations of shape A moving up to B, which in turn moved away. Animations factorially varied the temporal and spatial relations of the shapes, and whether they moved rigidly or in a non-rigid, animal-like manner. Impressions of social as well as physical causality appeared in both free reports and ratings. Perception of physical causality was stronger than perception of social causality, particularly in free reports. No differences of this nature appear in infants and children, so the asymmetry may reflect learnt knowledge. Physical causality was relatively unspecific initially, but discrimination of causal and delayed control events improved with exposure to multiple events. Experience seems to affect the causal illusion even over a short timeframe; the idea of 'one-trial causality' may be somewhat misleading. Regardless of such effects on the absolute level of responses, the different measures showed similar patterns of variation with the spatio-temporal configuration and type of motion. The good fit of ratings and reports validates much recent work in this area.
Adolescent↗
Measuring causal perception: connections to representational momentum?
In a collision between two objects, we can perceive not only low-level properties, such as color and motion, but also the seemingly high-level property of causality. It has proven difficult, however, to measure causal perception in a quantitatively rigorous way which goes beyond perceptual reports. Here we focus on the possibility of measuring perceived causality using the phenomenon of representational momentum (RM). Recent studies suggest a relationship between causal perception and RM, based on the fact that RM appears to be attenuated for causally 'launched' objects. This is explained by appeal to the visual expectation that a 'launched' object is inert and thus should eventually cease its movement after a collision, without a source of self-propulsion. We first replicated these demonstrations, and then evaluated this alleged connection by exploring RM for different types of displays, including the contrast between causal launching and non-causal 'passing'. These experiments suggest that the RM-attenuation effect is not a pure measure of causal perception, but rather may reflect lower-level spatiotemporal correlates of only some causal displays. We conclude by discussing the strengths and pitfalls of various methods of measuring causal perception.
Cognition↗
Categories and causality: the neglected direction.
The standard approach guiding research on the relationship between categories and causality views categories as reflecting causal relations in the world. We provide evidence that the opposite direction also holds: categories that have been acquired in previous learning contexts may influence subsequent causal learning. In three experiments we show that identical causal learning input yields different attributions of causal capacity depending on the pre-existing categories to which the learning exemplars are assigned. There is a strong tendency to continue to use old conceptual schemes rather than switch to new ones even when the old categories are not optimal for predicting the new effect, and when they were motivated by goals that differed from the present context of causal discovery. However, we also found that the use of prior categories is dependent on the match between categories and causal effect. Whenever the category labels suggest natural kinds which can be plausibly related to the causal effects, transfer was observed. When the categories were arbitrary, or could not be plausibly related to the causal effect learners abandoned the categories, and used different categories to predict the causal effect.
Humans↗
Causal associations between hormone replacement therapy and brain structure: Evidence from large-scale Mendelian randomization and double machine learning.
BACKGROUND: Hormone replacement therapy (HRT) is widely prescribed for the management of hormone deficiency, particularly during menopause, yet its causal effects on human brain structure remain incompletely understood. Observational studies have reported heterogeneous associations, underscoring the need for robust causal inference. METHODS: We applied an integrated causal framework combining two-sample Mendelian Randomization (MR) and Double Machine Learning (DML) to evaluate the effects of four HRT-related exposures-age at initiation, age at cessation, ever-use of HRT, and a composite medication-based phenotype-on 1366 brain imaging-derived phenotypes from the UK Biobank. Genetic instruments were derived from large-scale GWAS summary statistics, and causal estimates were validated using non-parametric DML models with cross-fitting and performance evaluation. RESULTS: Genetic instruments for age at HRT initiation, age at cessation, and ever-use of HRT were strong (median F-statistics 16.29-36.66). MR analyses identified a causal association between later initiation of HRT and lower orientation dispersion in the right inferior cerebellar peduncle (ubm-a-542; primary finding, no pleiotropy detected). An additional association with the left tapetum FA (ubm-a-243) was identified but exhibited significant directional horizontal pleiotropy (MR-Egger intercept P = 0.001) and is excluded from primary conclusions (Supplementary Note S2). Later cessation of HRT was associated with increased cortical thickness in the left middle occipital gyrus, reduced surface area in the left frontopolar cortex, and increased orientation dispersion in the splenium of the corpus callosum. Ever-use of HRT was causally linked to larger volumes of the right inferior frontal gyrus and right nucleus accumbens. These associations were corroborated by independent DML validation, which provided causally debiased estimates robust to high-dimensional confounding. Results for ukb-b-8080 (median F = 1.45) are provided in Supplementary Note S1 only; weak-instrument bias precludes causal inference. CONCLUSIONS: This study provides genetic-instrument-based and machine-learning-validated evidence for causal associations between HRT exposure-particularly its timing and lifetime use-and specific features of human brain structure, including white-matter microarchitecture, cortical thickness, and regional brain volume. These findings are FDR-controlled within exposures and independently replicated by DML, but require replication in external neuroimaging GWAS cohorts to establish definitive causal conclusions. They highlight the neurobiological relevance of sex steroid exposure and inform future research on brain aging and personalized hormone-based interventions.
Humans↗
Rethinking temporal contiguity and the judgement of causality: effects of prior knowledge, experience, and reinforcement procedure.
Time plays a pivotal role in causal inference. Nonetheless most contemporary theories of causal induction do not address the implications of temporal contiguity and delay, with the exception of associative learning theory. Shanks, Pearson, and Dickinson (1989) and several replications (Reed, 1992, 1999) have demonstrated that people fail to identify causal relations if cause and effect are separated by more than two seconds. In line with an associationist perspective, these findings have been interpreted to indicate that temporal lags universally impair causal induction. This interpretation clashes with the richness of everyday causal cognition where people apparently can reason about causal relations involving considerable delays. We look at the implications of cause-effect delays from a computational perspective and predict that delays should generally hinder reasoning performance, but that this hindrance should be alleviated if reasoners have knowledge of the delay. Two experiments demonstrated that (1) the impact of delay on causal judgement depends on participants' expectations about the timeframe of the causal relation, and (2) the free-operant procedures used in previous studies are ill-suited to study the direct influences of delay on causal induction, because they confound delay with weaker evidence for the relation in question. Implications for contemporary causal learning theories are discussed.
Adult↗
How the brain perceives causality: an event-related fMRI study.
Detection of the causal relationships between events is fundamental for understanding the world around us. We report an event-related fMRI study designed to investigate how the human brain processes the perception of mechanical causality. Subjects were presented with mechanically causal events (in which a ball collides with and causes movement of another ball) and non-causal events (in which no contact is made between the balls). There was a significantly higher level of activation of V5/MT/MST bilaterally, the superior temporal sulcus bilaterally and the left intraparietal sulcus to causal relative to non-causal events. Directing attention to the causal nature of the stimuli had no significant effect on the neural processing of the causal events. These results support theories of causality suggesting that the perception of elementary mechanical causality events is automatically processed by the visual system.
Adult↗
Perceived causality as a cue to temporal distance.
The three experiments reported show that judgments of elapsed time between events depend on perceived causal relations between the events. Participants judged pairs of causally related events to occur closer together in time than pairs of causally unrelated events that were separated by the same actual time interval. The causality-time relationship was first demonstrated for time judgments about historical events. Causally related events were judged to be significantly closer together in time than causally unrelated events. In two subsequent experiments, perceived causality was manipulated by providing expert information and by asking the participants themselves to imagine causal relationships between the to-be-judged events. Again, substantial and reliable effects of perceived causality were obtained. Our results suggest that people use strength of perceived causality as a cue to infer temporal distance.
Attention↗
Transcriptome-wide root causal inference.
Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm has been designed to discover root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously determines the sequence in which gene expression changes propagate through the system to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.
Algorithms↗