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Results for “causal inference”

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PubMed2006

Wild rhesus monkeys generate causal inferences about possible and impossible physical transformations in the absence of experience.

Human infants and adults generate causal inferences about the physical world from observations of single, novel events, thereby violating Hume's thesis that spatiotemporal cooccurrence from prior experience drives causal perception in our species. Is this capacity unique or shared with other animals? We address this question by presenting the results of three experiments on free-ranging rhesus monkeys (Macaca mulatta), focusing specifically on their capacity to generate expectations about the nature of completely unfamiliar physical transformations. By using an expectancy violation looking-time method, each experiment presented subjects with either physically possible or impossible transformations of objects (e.g., a knife, as opposed to a glass of water, appears to cut an apple in half). In both experiments, subjects looked longer when the transformation was impossible than when it was possible. Follow up experiments ruled out that these patterns could be explained by association. These results show that in the absence of training or direct prior experience, rhesus monkeys generate causal inferences from single, novel events, using their knowledge of the physical world to guide such expectations.

Animals↗

PubMed2001

Causal learning mechanisms in very young children: two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation.

Three studies investigated whether young children make accurate causal inferences on the basis of patterns of variation and covariation. Children were presented with a new causal relation by means of a machine called the "blicket detector." Some objects, but not others, made the machine light up and play music. In the first 2 experiments, children were told that "blickets make the machine go" and were then asked to identify which objects were "blickets." Two-, 3-, and 4-year-old children were shown various patterns of variation and covariation between two different objects and the activation of the machine. All 3 age groups took this information into account in their causal judgments about which objects were blickets. In a 3rd experiment, 3- and 4-year-old children used the information when they were asked to make the machine stop. These results are related to Bayes-net causal graphical models of causal learning.

Adult↗

PubMed2000

Interpreting epidemiological evidence: how meta-analysis and causal inference methods are related.

Interpreting observational epidemiological evidence can involve both the quantitative method of meta-analysis and the qualitative criteria-based method of causal inference. The relationships between these two methods are examined in terms of the capacity of meta-analysis to contribute to causal claims, with special emphasis on the most commonly used causal criteria: consistency, strength of association, dose-response, and plausibility. Although meta-analysis alone is not sufficient for making causal claims, it can provide a reproducible weighted average of the estimate of effect that seems better than the rules-of-thumb (e.g. majority rules and all-or-none) often used to assess consistency. A finding of statistical heterogeneity, however, need not preclude a conclusion of consistency (e.g. consistently greater than 1.0). For the criteria of strength of association and dose-response, meta-analysis provides more precise estimates, but the causal relevance of these estimates remains a matter of judgement. Finally, meta-analysis may be used to summarize evidence from biological, clinical, and social levels of knowledge, but combining evidence across levels is beyond its current capacity. Meta-analysis has a real but limited role in causal inference, adding to an understanding of some causal criteria. Meta-analysis may also point to sources of confounding or bias in its assessment of heterogeneity.

Causality↗

PubMed2026

TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries.

The limited genetic diversity in genome-wide association studies (GWAS) poses a significant challenge to the generalizability and equity of biomedical discoveries. Most causal inferences, particularly from high-dimensional phenomes (e.g. metabolomics), are primarily based on European populations, and their applicability to other ancestries remains uncertain. Traditional multivariable Mendelian randomization (MVMR) methods further struggle in high-dimensional and correlated settings due to collinearity and model instability. To bridge this gap, we present a two-step transfer learning framework for high-dimensional MR (TL-HDMR), designed to enhance causal exposure detection in understudied populations. Our approach leverages the Minimax Concave Penalty for asymptotically unbiased estimation amidst exposure correlations. Crucially, we introduce two novel pre-transfer procedures-HDMR.TSD for sourcing beneficial data and HDMR.PRESSO for filtering pleiotropic instruments-to ensure robust knowledge transfer. Extensive simulations demonstrated TL-HDMR's superior performance in ROC curves and mean absolute error over alternative methods. When applied to identify causal metabolites for stroke across multi-ancestry cohorts (European, East Asian, South Asian, and African), TL-HDMR successfully pinpointed both shared and ethnic-specific causal biomarkers, showcasing its unique capability for equitable causal inference. This work provides a powerful statistical tool that not only addresses critical methodological challenges but also promotes inclusivity and fairness in human health research.

Humans↗

PubMed1989

Young children's causal inferences.

We report 2 experiments that show a striking development between the ages of 3 and 4 years in children's ability to make causal inferences about sequences of events. The task in the first experiment was to work out what had caused the change to an object that started out as odd (noncanonical) in 1 way and ended up as odd in 2 ways--starting, for example, as a broken cup and ending as a wet and broken cup. When asked to choose the instrument that had caused the change, 3-year-olds often selected the instrument that could have caused the initial state (a hammer, in our example) and not the instrument that would produce the change. 4-year-olds hardly ever made this mistake. In the second experiment, the 3-year-olds were able to make the correct choice when the change was from a canonical to a noncanonical state (cup-wet cup) but had much more difficulty when the change was from noncanonical to canonical (wet cup-dry cup). The difference was much smaller in the older group. The first of these tasks can be solved simply on the basis of knowledge that a particular instrument can cause a particular effect without reference to the initial state. The second task requires attention to the differences between initial and final state. We conclude that the ability to make genuine causal inferences develops between the ages of 3 and 4 years.

Child Development↗

PubMed2025

Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes.

With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the individual gene expression levels of interest are not directly observable; instead, only repeated proxy measurements from each individual's cells are available, providing a derived outcome to estimate the underlying outcome for each of many genes. In this paper, we propose a generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes, which also encompasses the usual setting of multiple outcomes when the response of each unit is available. To reliably quantify the causal effects of heterogeneous outcomes, we specialize the analysis to standardized average treatment effects and quantile treatment effects. Through this, we demonstrate the use of the semiparametric inferential results for doubly robust estimators derived from both Von Mises expansions and estimating equations. A multiple testing procedure based on Gaussian multiplier bootstrap is tailored for doubly robust estimators to control the false discovery exceedance rate. Applications in single-cell CRISPR perturbation analysis and individual-level differential expression analysis demonstrate the utility of the proposed methods and offer insights into the usage of different estimands for causal inference in genomics.

Derived outcomes↗

PubMed2003

Causal inference in lead research: introduction to the special section on the neurobehavioral effects of environmental lead.

This Special Section on the Neurobehavioral Effects of Environmental Lead includes empirical reports from three major longitudinal prospective studies and a review of animal research modeling effects of lead on human cognition. This Introductory article addresses a fundamental controversy in lead and other epidemiologic research. It is argued that, contrary to conventional wisdom, valid causal inferences can be made from correlational research. Justification for this is to be found in contemporary frameworks drawn from the field of the philosophy of science; an essential, but often neglected perspective for such metatheoretical issues as causal inference. It is concluded that the nonexperimental methods described in the articles comprising this Special Section can, indeed, contribute to causal formulations of lead effects on human development, and that such inferences are not exclusive to experimental evidence.

Causality↗

PubMed2000

Accident reduction factors and causal inference in traffic safety studies: a review.

Accident reduction factors are used to predict the change in accident occurrence which a countermeasure can be expected to cause. Since ethical and legal obstacles preclude the use of randomized experiments when evaluating traffic safety improvements, empirical support for the causal effectiveness of accident countermeasures comes entirely from observational studies. Drawing on developments in causal inference initiated by Donald Rubin, it is argued here that the mechanism by which sites are selected for application of a countermeasure should be included as part of a study's data model, and that when important features of the selection mechanism are neglected, existing methods for estimating accident reduction factors become inconsistent. A promising, but neglected, way out of these difficulties lies in developing rational countermeasure selection methods which also support valid causal inference of countermeasure effects.

Accidents, Traffic↗

PubMed1996

Concerns about drawing causal inferences from meta-analyses: an example in the study of gender differences in aggression.

Meta-analysis has increasingly been used as an explanatory research tool. The present investigation was designed to illustrate the potential limitations of meta-analysis for making causal inferences. Several meta-analytic investigations have led others to conclude that gender differences are getting smaller over time, however, there has been little concern regarding changes in research methodology over time. The present findings indicate that the gender differences in aggression appear to be remarkably stable when changes in study characteristics over time are controlled. The authors discuss the implications for the use of meta-analytic procedures to make causal inferences and the implications for understanding the causes of gender differences.

Aggression↗

PubMed1990

Concept availability in the causal inferences of children and adults.

What supplies the concepts in causal inferences in story comprehension? This question was examined in 5 experiments. Elementary school children and college students listened to stories containing a "premise" sentence describing a character's intent in initiating a series of actions, followed by an unexpected "outcome." After each story, the subjects were asked inference questions about the reason for the outcome (Experiments 1-4) or asked to explain the outcome (Experiment 5). In the various experiments, the availability of object concepts for an inference was manipulated by providing 0 or 2 clues preceding the outcome, additional filler information that diluted the clue information, a mention of an object in the premise or no mention, and an object title for each story or no title. The results suggested that the availability of an object concept in sentences prior to the outcome was a critical determinant of whether an object inference occurred, especially for children. Given availability, the thematic prominence of the object influences whether the object will be used in an inference.

Adolescent↗

PubMed2001

A psychometric experiment in causal inference to estimate evidential weights used by epidemiologists.

A psychometric experiment in causal inference was performed on 159 Australian and New Zealand epidemiologists. Subjects each decided whether to attribute causality to 12 summaries of evidence concerning a disease and a chemical exposure. The 1,748 unique summaries embodied predetermined distributions of 19 characteristics generated by computerized evidence simulation. Effects of characteristics of evidence on causal attribution were estimated from logistic regression, and interactions were identified from a regression tree analysis. Factors with the strongest influence on the odds of causal attribution were statistical significance (odds ratio = 4.5 if 0.001 < or = P < 0.05 and 7.2 if P < 0.001, vs P > or = 0.05); refutation of alternative explanations (odds ratio = 8.1 for no known confounder vs none adjusted); strength of association (odds ratio = 2.0 if 1.5 < relative risk < or = 2.0 and 3.6 if relative risk > 2.0, vs relative risk < or = 1.5); and adjunct information concerning biological, factual, and theoretical coherence. The refutation of confounding reduced the cutpoint in the regression tree for decision-making based on strength of association. The effect of the number of supportive studies reached saturation after it exceeded 12 studies. There was evidence of flawed logic in the responses concerning specificity of effects of exposure and a tendency to discount evidence if the P-value was a "near miss" (0.050 < P < 0.065). Evidential weights based on regression coefficients for causal criteria can be applied to actual scientific evidence.

Adult↗

PubMed1995

Causal inferences in reading: from immediate activation to long-term memory.

Three different tasks were used to investigate the time course of drawing causal inferences. Participants read passages that contained a causal coherence break that could be resolved by reactivating a concept presented earlier in the passage. In Experiment 1, participants named a probe word that represented the earlier mentioned cause more quickly after encountering the causal coherence break, suggesting that the causal concept had quickly been reactivated. In Experiment 2, participants were slow to read a sentence after the causal coherence break that contradicted the intended inference, indicating that the inference had been encoded and retained in working memory. In Experiment 3, the results of a recall task indicated that the causal link was also included in the long-term memory representation of the text.

Humans↗

PubMed2004

How the brain processes causal inferences in text.

Theoretical models of text processing, such as the construction-integration framework, pose fundamental questions about causal inference making that are not easily addressed by behavioral studies. In particular, a common result is that causal relatedness has a different effect on text reading times than on memory for the text: Whereas reading times increase linearly as causal relatedness decreases, memory for the text is best for events that are related by a moderate degree of causal relatedness and is poorer for events with low and high relatedness. Our functional magnetic resonance imaging study of the processing of two-sentence passages that varied in their degree of causal relatedness suggests that the inference process can be analyzed into two components, generation and integration, that are subserved by two large-scale cortical networks (a reasoning system in dorsolateral prefrontal cortex and the right-hemisphere language areas). These two cortical networks, which are distinguishable from the classical left-hemisphere language areas, approximately correspond to the two functional relations observed in the behavioral results.

Brain↗

PubMed2005

Contiguity and covariation in human causal inference.

Nearly every theory of causal induction assumes that the existence and strength of causal relations needs to be inferred from observational data in the form of covariations. The last few decades have seen much controversy over exactly how covariations license causal conjectures. One consequence of this debate is that causal induction research has taken for granted that covariation information is readily available to reasoners. This perspective is reflected in typical experimental designs, which either employ covariation information in summary format or present participants with clearly marked discrete learning trials. I argue that such experimental designs oversimplify the problem of causal induction. Real-world contexts rarely are structured so neatly; rather, the decision about whether a cause and effect co-occurred on a given occasion constitutes a key element of the inductive process. This article will review how the event-parsing aspect of causal induction has been and could be addressed in associative learning and causal power theories.

Association Learning↗

PubMed2004

Inflammatory brain damage in preterm newborns--dry numbers, wet lab, and causal inferences.

Epidemiologic observations support the contention that infection, inflammation, and neonatal white matter damage (WMD) are associated. We also have documentation from multiple experimental models that infection/inflammation can damage developing white matter. Based on these observations in humans and animals, we offer causal inferences using widely accepted causal criteria and the multivariable model of causation. As much as we want to, however, we are reluctant to state unequivocally that inflammation causes WMD in humans born much before term. The main reason is that we lack convincing evidence that inflammation precedes WMD (temporal evidence). We also need more (and more detailed) observational studies clarifying the presumed infection --> inflammation --> WMD sequence before we can initiate intervention trials to reduce the risk of WMD.

Analysis of Variance↗

PubMed1993

Towards causal inference in occupational cancer epidemiology--II. Getting the count right.

This second paper in the series assessing causal inference in occupational cancer epidemiology highlights the importance of using the right data sources when examining the association between exposure and tumour incidence in groups of workers. Using the wrong ones can lead to omissions which become insidious and cumulative during a long-term follow-up study, often extending over several decades. The main cause is the changing survival pattern of some cancers and it is a new and important factor to be taken into account. This was illustrated during an in-depth study of an excess of bladder cancer at a large tire factory, where the workforce was exposed to beta-naphthylamine-contaminated antioxidants used in processing. At first the study was confined to a mortality analysis using death certificates to identify those whose underlying cause of death was bladder cancer [International Classification of Diseases (8th Rev.), No. 188]. It soon became apparent, however, that several cases had outlived the study end-date, or had died of an unrelated cause, often with bladder cancer not being mentioned anywhere on the death certificate. The study was then changed also to include morbidity (incidence) data using cancer registration. Between 1946 and 1949 some 2090 men were exposed to two antioxidants contaminated with about 0.2% of beta-naphthylamine. Altogether there were 45 bladder tumours relevant to this 'at risk' group, whereas only 24.8 were expected at the national incidence rates (P < 0.001), which clearly reflects the group's exposure to a recognized bladder carcinogen.(ABSTRACT TRUNCATED AT 250 WORDS)

Causality↗

PubMed1992

Who caused it? Interpersonal causal inferences in young children.

Three experiments were designed to test 4- and 6-year-old children's causal inferences in interpersonal settings where emotions (glad, angry, and sad) were effect responses. The results showed that emotion and orientation (towards or away from) were central cues, and that sex and age also were used to some extent. Cues related to regularity philosophic notions (e.g. David Hume), such as contiguity in time and space, and time order of cause and effect were little used by comparison. The results raise questions about the basic role attributed to regularity cues both by philosophers and psychologists, and suggest a multiple cue contribution rather than a basic cue generalization approach to causal cognition development.

Child↗

PubMed2001

Childhood victimization and alcohol symptoms in females: causal inferences and hypothesized mediators.

OBJECTIVE: Previous research has reported a relationship between childhood victimization experiences and alcohol problems in females. This paper has two distinct goals: (1) to determine whether it is appropriate to make a causal inference regarding the association between early child abuse and neglect and alcohol symptoms in females; and (2) to examine five potential mechanisms (depression, worthlessness, social isolation/loneliness, low self esteem, and using alcohol and/or drugs to cope) that may mediate the relationship between child abuse and neglect and alcohol symptomatology. METHOD: Substantiated cases of child abuse and neglect from 1967 to 1971 were matched on sex, age, race, and approximate social class with nonabused and non-neglected children and followed prospectively into young adulthood. Subjects were administered a 2-h in-person interview, including the NIMH Diagnostic Interview Schedule (DIS-III-R) to assess alcohol use and abuse. Analyses are restricted to females in the sample (N = 582). RESULTS: Structural equation modeling provides support for the inference that childhood victimization plays a causal role in the development of alcohol symptoms in women. There also is support for the hypothesized mediating role of two mechanisms (depression and using alcohol/drugs to cope), but not for the other mediators. CONCLUSIONS: Evidence from this prospective study suggests that childhood victimization may be one of the causal factors in the development of alcohol problems in females. Interventions should bedirected at abused and neglected females of all ages to help them to deal with depression and to develop coping strategies to prevent the development of serious alcohol problems.

Adaptation, Psychological↗