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PubMed2004

Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies.

Inferring causal effects from longitudinal repeated measures data has high relevance to a number of areas of research, including economics, social sciences and epidemiology. In observational studies in particular, the treatment receipt mechanism is typically not under the control of the investigator; it can depend on various factors, including the outcome of interest. This results in differential selection into treatment levels, and can lead to selection bias when standard routines such as least squares regression are used to estimate causal effects. Interestingly, both the characterization of and methodology for handling selection bias can differ substantially by disciplinary tradition. In social sciences and economics, instrumental variables (IV) is the standard method for estimating linear and nonlinear models in which the error term may be correlated with an observed covariate. When such correlation is not ruled out, the covariate is called endogenous and least squares estimates of the covariate effect are typically biased. The availability of an instrumental variable can be used to reduce or eliminate the bias. In public health and clinical medicine (e.g., epidemiology and biostatistics), selection bias is typically viewed in terms of confounders, and the prevailing methods are geared toward making proper adjustments via explicit use of observed confounders (e.g., stratification, standardization). A class of methods known as inverse probability weighting (IPW) estimators, which relies on modeling selection in terms of confounders, is gaining in popularity for making such adjustments. Our objective is to review and compare IPW and IV for estimating causal treatment effects from longitudinal data, where the treatment may vary with time. We accomplish this by defining the causal estimands in terms of a linear stochastic model of potential outcomes (counterfactuals). Our comparison includes a review of terminology typically used in discussions of causal inference (e.g., confounding, endogeneity); a review of assumptions required to identify causal effects and their implications for estimation and interpretation; description of estimation via inverse weighting and instrumental variables; and a comparative analysis of data from a longitudinal cohort study of HIV-infected women. In our discussion of assumptions and estimation routines, we try to emphasize sufficient conditions needed to implement relatively standard analyses that can essentially be formulated as regression models. In that sense this review is geared toward the quantitative practitioner. The objective of the data analysis is to estimate the causal (therapeutic) effect of receiving combination antiviral therapy on longitudinal CD4 cell counts, where receipt of therapy varies with time and depends on CD4 count and other covariates. Assumptions are reviewed in context, and resulting inferences are compared. The analysis illustrates the importance of considering the existence of unmeasured confounding and of checking for 'weak instruments.' It also suggests that IV methodology may have a role in longitudinal cohort studies where potential instrumental variables are available.

Antiretroviral Therapy, Highly Active↗

PubMed2026

Causal Effects Between Neurodegenerative Diseases, Metabolites, and Brain Volume.

INTRODUCTION/OBJECTIVE: Neurodegenerative diseases such as Alzheimer's disease (AD), Lewy dody dementia (LBD), and Parkinson's disease (PD) are linked to changes in brain volume. However, causal evidence on how these diseases affect brain volume and whether metabolites mediate these causal effects remains limited. METHODS: We applied mediation Mendelian randomization analysis using GWAS summary statistics. The inverse variance-weighted method was used to assess causal effects and identify potential metabolite mediators. RESULTS: The MR analyses indicated that bilateral thalamus and putamen volumes (FDR < 0.05) had causal effects on PD. AD and LBD showed causal effects on bilateral thalamus and hippocampus (FDR < 0.01), with LBD specifically showing a causal effect on bilateral putamen (FDR < 0.05). Mediation analyses revealed that AD had a genetically predicted association with Nervonoy- L-carnitine and 1-linoleoyl-2-arachidonoyl-GPC (p-value = 0.04 and 0.01, respectively). Moreover, Nervonoy-L-carnitine was suggestively negatively associated with hippocampus volume (p-value = 0.03 and 0.02, respectively). 1-linoleoyl-2-arachidonoyl-GPC exhibited a negative genetically predicted association with hippocampus volume (p-value < 0.05). Additionally, LBD showed a negative genetically predicted association on the ratio of retinol to linoleoyl-arachidonoyl- glycerol (p-value = 0.02), and a positive genetically predicted association on Nervonoy-L-- carnitine (p-value < 0.05) and 1-linoleoyl-2-arachidonoyl-GPC (p-value = 0.03). DISCUSSION: These results suggest that AD and LBD affect brain regions through causal pathways. The involvement of specific metabolites highlights potential mechanisms linking neurodegeneration to brain volume. CONCLUSION: Nervonoylcarnitine and 1-linoleoyl-2-arachidonoyl-GPC may mediate the predicted effects of AD and LBD on hippocampal volumes, while the ratio of retinol to linoleoyl-arachidonoyl- glycerol mediates only LBD.

Humans↗

PubMed2002

How temporal assumptions influence causal judgments.

Causal learning typically entails the problem of being confronted with a large number of potentially relevant statistical relations. One type of constraint that may guide the choice of appropriate statistical indicators of causality are assumptions about temporal delays between causes and effects. There have been a few previous studies in which the role of temporal relations in the learning of events that are experienced in real time have been investigated. However, human causal reasoning may also be based on verbally described events, rather than on direct experiences of the events to which the descriptions refer. The aim of this paper is to investigate whether assumptions about the temporal characteristics of the events that are being described also affect causal judgment. Three experiments are presented that demonstrate that different temporal assumptions about causal delays may lead to dramatically different causal judgments, despite identical leaning inputs. In particular, the experiments show that temporal assumptions guide the choice of appropriate statistical indicators of causality by structuring the event stream (Experiment 1), by selecting the potential causes among a set of competing candidates (Experiment 2), and by influencing the level of aggregation of events (Experiment 3).

Humans↗

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↗

PubMed2005

A dissociation between causal judgment and outcome recall.

It has been suggested that causal learning in humans is similar to Pavlovian conditioning in animals. According to this view, judgments of cause reflect the degree to which an association exists between the cause and the effect. Inferential accounts, by contrast, suggest that causal judgments are reasoning based rather than associative in nature. We used a direct measure of associative strength, identification of the outcome with which a cause was paired (cued recall), to see whether associative strength translated directly into causal ratings. Causal compounds AB+ and CD+ were intermixed withA+ and C- training. Cued-recall performance was better for cue B than for cue D; thus, associative strength was inherited by cue B from the strongly associated cue A (augmentation). However, the reverse was observed on the causal judgment measure: Cue B was judged to be less causal than D (cue competition). These results support an inferential over an associative account of causal judgments.

Association↗

PubMed1995

Naive theories and causal deduction.

Evidence is presented that implicates two factors in deductive reasoning about causality. The factors are alternative causes and disabling conditions (factors that prevent effects from occurring in the presence of viable causes). A causal analysis is presented in which these factors impact on judgments concerning causal necessity and sufficiency, which in turn determine deductive entailment relations. In Experiment 1, these factors were found to impact causal deductive judgments more strongly than did logical form. In Experiment 2, causal deductive judgments were found to vary as a function of familiarity with a particular causal relationship: The more familiar the causal relationship, the less willing reasoners were to accept conclusions based on them.

Adult↗

PubMed1989

Ascertaining the causal factors for "ejection-associated" injuries.

Determining the cause(s) for an ejectee's injuries is one of the more important and yet most difficult tasks associated with an ejection investigation. Selection of causal factors is often distorted by rumors and inaccurate teaching concerning how specific types of injuries occur and/or the consequences of using specific types of Aircrew Automated Escape Systems (AAES) and Aircrew Life Support Systems (ALSS) equipment. Unfortunately, aiding and abetting the selection of incorrect causal factors is the "strength-in-numbers"-type legitimacy that many of these factors have acquired through frequent usage over the years. Thus, if one should query the Naval Safety Center computer concerning either 1) how many ejectees under certain conditions and/or using specific AAES/ALSS equipment sustained specific injuries, or 2) how many ejectees received a specific type of injury caused by a specific factor, that computer will obligingly and non-critically provide numbers. Because those numbers are generated by a computer, they gain an overwhelming appearance of irrefutability in demonstrating the correctness of any assessment that conforms. Careful, detailed investigation (and also general statistical investigation), however, often has revealed that these accepted causal factors either cannot be applicable or are of extremely doubtful applicability for the specific situations. This paper discusses some of the recent results of investigating many of the accepted causal factors and some methodologies that might aid an investigator in determining the causal factors. Also discussed is the value of admitting to not knowing the causal factor and the harm that can arise from guessing or joining the crowd in stating a causal factor in the Flight Surgeon's Report (FSR).

Aerospace Medicine↗

PubMed2005

Agreement of expert judgment in causality assessment of adverse drug reactions.

BACKGROUND: Global introspection is, with operational algorithms and Bayes' theorem, one of the three main approaches used to assess the causal relationship between a drug treatment and the occurrence of an adverse event. OBJECTIVE: To analyze and compare the judgments of five senior experts using global introspection about drug causation on a random set of putative adverse drug reactions. METHODS: A random sample of 150 drug-effect pairs was constituted. For each pair, five senior experts had to independently assess the probability of drug causation from 0 to 1 by using a 100 mm visual analog scale (VAS). For analysis, those probabilities were secondarily split into seven levels of causality: excluded (0-0.05); unlikely (0.06-0.25); doubtful (0.26-0.45); unassessable/unclassifiable (0.46-0.55); plausible (0.56-0.75); likely (0.75-0.95); and certain (0.95-1). Agreement among the five experts was assessed using kappa coefficients (kappa). RESULTS: The overall agreement between experts was poor (kappa=0.20), although significantly different from chance, and varied according to the level of causality. It was lower for the unlikely, doubtful, unassessable/unclassifiable, and plausible categories (kappa=0.03, 0.03, -0.01, and 0.13, respectively) than for VAS extremes: excluded, likely, and certain (kappa=0.40, 0.32, and 0.30, respectively). CONCLUSION: This study confirms that experts express marked disagreements when assessing drug causality independently. The agreement rate was lower for intermediate levels of causality, especially when strong evidence was lacking for confirming or ruling out drug causality. Therefore, in a decision-making context, a step-by-step consensual approach such as the Delphi method seems necessary to make the assessment of such cases more reliable.

Adverse Drug Reaction Reporting Systems↗

PubMed2005

Causal proportional hazards models and time-constant exposure in randomized clinical trials.

The last decade saw enormous progress in the development of causal inference tools to account for noncompliance in randomized clinical trials. With survival outcomes, structural accelerated failure time (SAFT) models enable causal estimation of effects of observed treatments without making direct assumptions on the compliance selection mechanism. The traditional proportional hazards model has however rarely been used for causal inference. The estimator proposed by Loeys and Goetghebeur (2003, Biometrics vol. 59 pp. 100-105) is limited to the setting of all or nothing exposure. In this paper, we propose an estimation procedure for more general causal proportional hazards models linking the distribution of potential treatment-free survival times to the distribution of observed survival times via observed (time-constant) exposures. Specifically, we first build models for observed exposure-specific survival times. Next, using the proposed causal proportional hazards model, the exposure-specific survival distributions are backtransformed to their treatment-free counterparts, to obtain - after proper mixing - the unconditional treatment-free survival distribution. Estimation of the parameter(s) in the causal model is then based on minimizing a test statistic for equality in backtransformed survival distributions between randomized arms.

Belgium↗

PubMed2003

Assessing the validity of road safety evaluation studies by analysing causal chains.

This paper discusses how the validity of road safety evaluation studies can be assessed by analysing causal chains. A causal chain denotes the path through which a road safety measure influences the number of accidents. Two cases are examined. One involves chemical de-icing of roads (salting). The intended causal chain of this measure is: spread of salt --> removal of snow and ice from the road surface --> improved friction --> shorter stopping distance --> fewer accidents. A Norwegian study that evaluated the effects of salting on accident rate provides information that describes this causal chain. This information indicates that the study overestimated the effect of salting on accident rate, and suggests that this estimate is influenced by confounding variables the study did not control for. The other case involves a traffic club for children. The intended causal chain in this study was: join the club --> improve knowledge --> improve behaviour --> reduce accident rate. In this case, results are rather messy, which suggests that the observed difference in accident rate between members and non-members of the traffic club is not primarily attributable to membership in the club. The two cases show that by analysing causal chains, one may uncover confounding factors that were not adequately controlled in a study. Lack of control for confounding factors remains the most serious threat to the validity of road safety evaluation studies.

Accident Prevention↗

PubMedDate not supplied

[Causality in occupational health: the Ardystil case].

Establishing causal relationships has been and is today a matter of debate in epidemiology. The observational nature of epidemiological research rends difficult the proving of these relationships. Related to this, different models and causal criteria have been proposed in order to explain health and disease determinants, from pure determinism in Koch postulates, accepting unicausal explanation for diseases, to more realistic multicausal models. In occupational health it is necessary to formulate causal models and criteria to assess causality, and frequently causal assessment in this field has important social, economic and juridical relevance. This paper deal with evaluation of causal relationships in epidemiology and this evaluation is illustrated with a recent example of an occupational health problem in our milieu: the Ardystil case.

Adult↗

PubMed2003

A causal-model theory of conceptual representation and categorization.

This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches.

Causality↗

PubMed2025

Circulating inflammatory proteins as causal drivers and therapeutic targets in asthma: insights from genetic and pathway-based analyses.

OBJECTIVE: To identify circulating inflammatory proteins with potential causal roles in asthma development through integrated genetic and pathway-based analyses, and to evaluate their potential as therapeutic targets. METHODS: We used genetically anchored instrumental variables from 180 protein quantitative trait loci (pQTLs) to assess the causal effects of 91 circulating inflammatory proteins on asthma risk, using large-scale GWAS datasets. Analytical robustness was evaluated through pleiotropy and heterogeneity testing. Functional enrichment and literature-based pathway analyses were performed to support biological plausibility and validate findings. RESULTS: Four proteins showed significant causal effects on asthma: CCL19 and LIFR were protective (OR = 0.89 and 0.91, p ≤ 6.8E-03), while ARTN and IL6 were associated with increased risk (OR = 1.15 and 1.18, p ≤ 1.1E-04). We also identified reverse causal effects of asthma on 11 cytokines, including MMP10, TGFB1, IL33, and IL18R1. Most of these proteins were enriched in pathways related to cytokine signaling and immune response (p < 0.001). All identified proteins had prior literature support linking them to asthma or airway inflammation. CONCLUSIONS: Our findings highlight a subset of circulating inflammatory proteins that are likely causal in asthma pathogenesis and may serve as promising targets for therapeutic intervention. These results offer novel insights into the immunological mechanisms underlying asthma and support the utility of genetic causal inference in target prioritization.

Asthma↗

PubMed2026

CAUSAL artificial intelligence and data-driven decision intelligence in personalized medicine: a review of healthcare informatics systems.

This review examines the integration of causal artificial intelligence (AI) and data-driven decision intelligence within healthcare informatics systems to advance personalized medicine and clinical decision-making. A narrative review methodology was employed, synthesizing interdisciplinary literature from major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Studies focusing on causal inference, decision intelligence, and healthcare informatics applications in personalized medicine were included. Data were extracted on methodological approaches, healthcare settings, analytical techniques, and clinical applications, followed by thematic synthesis. Findings indicate that causal AI enhances clinical decision support by enabling estimation of treatment effects and simulation of intervention outcomes at the individual patient level. Integration of multimodal health data such as electronic health records, genomic data, and real-time monitoring improves prediction accuracy and supports tailored treatment strategies. Additionally, causal models improve interpretability, fostering clinician trust and facilitating transparent decision-making. Robust healthcare informatics infrastructures, including interoperable systems and data warehouses, were identified as critical enablers of causal analytics. Overall, causal AI represents a transformative advancement in healthcare analytics, supporting more informed, individualized, and evidence-based clinical decisions. Its integration within healthcare informatics systems has significant potential to improve patient outcomes and guide the future of intelligent, personalized healthcare delivery.

Precision Medicine↗

PubMed2004

A definition of causal effect for epidemiological research.

Estimating the causal effect of some exposure on some outcome is the goal of many epidemiological studies. This article reviews a formal definition of causal effect for such studies. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a discussion of sampling variability and a generalisation of this causal theory. The difference between association and causation is described-the redundant expression "causal effect" is used throughout the article to avoid confusion with a common use of "effect" meaning simply statistical association-and shows why, in theory, randomisation allows the estimation of causal effects without further assumptions. The article concludes with a discussion on the limitations of randomised studies. These limitations are the reason why methods for causal inference from observational data are needed.

Causality↗

PubMed2000

Looking back on "causal thinking in the health sciences".

It has now been over a quarter of a century since the publication of Mervyn Susser's Causal Thinking in the Health Sciences (1973, Oxford University Press), the first book-length treatment of causal reasoning and inference in our field. Major contributions of this work were its holistic focus on the origins of health outcomes in the context of ecologic systems and its invigoration of the literature on causal criteria in epidemiology. Although a recent resurgence of interest in social context has revivified many points made by Susser, a formal basis for causal analysis consistent with this ecologic perspective has failed to emerge in public health research. Susser's discussion of causal criteria, on the other hand, helped spur a vigorous dialogue that has persisted unabated to the present day. Although the basic outline of the criteria has evolved little, their applications, interrelations, and relative contributions to causal judgments have been the subject of continued and sometimes contentious debate.

Causality↗

PubMed2005

Causal inference based on counterfactuals.

BACKGROUND: The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. DISCUSSION: This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. SUMMARY: Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.

Causality↗

PubMed2024

Assessing the causal link between liver function and acute pancreatitis: A Mendelian randomisation study.

A correlation has been reported to exist between exposure factors (e.g. liver function) and acute pancreatitis. However, the specific causal relationship remains unclear. This study aimed to infer the causal relationship between liver function and acute pancreatitis using the Mendelian randomisation method. We employed summary data from a genome-wide association study involving individuals of European ancestry from the UK Biobank and FinnGen. Single-nucleotide polymorphisms (SCNPs), closely associated with liver function, served as instrumental variables. We used five regression models for causality assessment: MR-Egger regression, the random-effect inverse variance weighting method (IVW), the weighted median method (WME), the weighted model, and the simple model. We assessed the heterogeneity of the SNPs using Cochran's Q test. Multi-effect analysis was performed using the intercept term of the MR-Egger method and leave-one-out detection. Odds ratios (ORs) were used to evaluate the causal relationship between liver function and acute pancreatitis risk. A total of 641 SNPs were incorporated as instrumental variables. The MR-IVW method indicated a causal effect of gamma-glutamyltransferase (GGT) on acute pancreatitis (OR = 1.180, 95%CI [confidence interval]: 1.021-1.365, P = 0.025), suggesting that GGT may influence the incidence of acute pancreatitis. Conversely, the results for alkaline phosphatase (ALP) (OR = 0.997, 95%CI: 0.992-1.002, P = 0.197) and aspartate aminotransferase (AST) (OR = 0.939, 95%CI: 0.794-1.111, P = 0.464) did not show a causal effect on acute pancreatitis. Additionally, neither the intercept term nor the zero difference in the MR-Egger regression attained statistical significance (P = 0.257), and there were no observable gene effects. This study suggests that GGT levels are a potential risk factor for acute pancreatitis and may increase the associated risk. In contrast, ALP and AST levels did not affect the risk of acute pancreatitis.

Humans↗