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Results for “causal inference”

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At least 19 recordsLinked to original sources
PubMed2006

Inferring causal relationships among intermediate phenotypes and biomarkers: a case study of rheumatoid arthritis.

MOTIVATION: Genetic association analysis is based on statistical correlations which do not assign any cause-to-effect arrows between the two correlated variables. Normally, such assignment of cause and effect label is not necessary in genetic analysis since genes are always the cause and phenotypes are always the effect. However, among intermediate phenotypes and biomarkers, assigning cause and effect becomes meaningful, and causal inference can be useful. RESULTS: We show that causal inference is possible by an example in a study of rheumatoid arthritis. With the help of genotypic information, the shared epitope, the causal relationship between two biomarkers related to the disease, anti-cyclic citrullinated peptide (anti-CCP) and rheumatoid factor (RF) has been established. We emphasize the fact that third variable must be a genotype to be able to resolve potential ambiguities in causal inference. Two non-trivial conclusions have been reached by the causal inference: (1) anti-CCP is a cause of RF and (2) it is unlikely that a third confounding factor contributes to both anti-CCP and RF.

Arthritis, Rheumatoid↗
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↗
PubMed2024

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↗
PubMed2025

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↗
PubMed1996

The practice of causal inference in cancer epidemiology.

Causal inference is an important link between the practice of cancer epidemiology and effective cancer prevention. Although many papers and epidemiology textbooks have vigorously debated theoretical issues in causal inference, almost no attention has been paid to the issue of how causal inference is practiced. In this paper, we review two series of review papers published between 1985 and 1994 to find answers to the following questions: which studies and prior review papers were cited, which causal criteria were used, and what causal conclusions and public health recommendations ensued. Fourteen published reviews on alcohol and breast cancer and 6 published reviews on vasectomy and prostate cancer were examined. For both series of reviews, nearly all available published studies were cited except for ecological studies and prior reviews. Sources of causal criteria were often not provided. When they appeared, all citations were either the 1964 Surgeon General's report or works of Austin Bradford Hill. Reviews often excluded and sometimes altered criteria without giving reasons for these changes. The criteria of consistency and strength of association were almost always used accompanied by dose-response and biological plausibility in a majority of reviews. The criterion of temporality, considered by many methodologists to be a necessary causal condition, was infrequently used. Confounding and bias were often added considerations. Public health recommendations were not discussed in nearly one-half of the reviews.

Breast Neoplasms↗
PubMed2005

On the use of discrete choice models for causal inference.

Methodology for causal inference based on propensity scores has been developed and popularized in the last two decades. However, the majority of the methodology has concentrated on binary treatments. Only recently have these methods been extended to settings with multi-valued treatments. We propose a number of discrete choice models for estimating the propensity scores. The models differ in terms of flexibility with respect to potential correlation between treatments, and, in turn, the accuracy of the estimated propensity scores. We present the effects of discrete choice models used on performance of the causal estimators through a Monte Carlo study. We also illustrate the use of discrete choice models to estimate the effect of antipsychotic drug use on the risk of diabetes in a cohort of adults with schizophrenia.

Adolescent↗
PubMed2000

[How does model- and situation-specific lack of knowledge affect causal inferences?].

A model proposed by Thüring (1991) for inferences based on causal knowledge was empirically tested. According to this model, two variables affect the certainty with which a causal inference is concluded: insufficiency (model-specific uncertainty) and ambiguity (situation-specific uncertainty). Within an experiment these two variables were manipulated. Both had a very significant (p < .01) influence on causal inferences. In respect to its quantity, variation of ambiguity had the effect predicted in the model. Concerning insufficiency, distinct differences between predicted and empirical ratings were found. Reasons for these deviations and model modifications resulting therefrom are discussed.

Adult↗
PubMed2005

Causation and causal inference in epidemiology.

Concepts of cause and causal inference are largely self-taught from early learning experiences. A model of causation that describes causes in terms of sufficient causes and their component causes illuminates important principles such as multi-causality, the dependence of the strength of component causes on the prevalence of complementary component causes, and interaction between component causes. Philosophers agree that causal propositions cannot be proved, and find flaws or practical limitations in all philosophies of causal inference. Hence, the role of logic, belief, and observation in evaluating causal propositions is not settled. Causal inference in epidemiology is better viewed as an exercise in measurement of an effect rather than as a criterion-guided process for deciding whether an effect is present or not.

Causality↗
PubMed1996

[Causal inference in medicine--a historical view in epidemiology].

Changes of causal inference concepts in medicine, especially those having to do with chronic diseases, were reviewed. The review is divided into five sections. First, several articles on the increased academic acceptance of observational research are cited. Second, the definitions of confounder and effect modifier concepts are explained. Third, the debate over the so-called "criteria for causal inference" was discussed. Many articles have pointed out various problems related to the lack of logical bases for standard criteria, however, such criteria continue to be misapplied in Japan. Fourth, the Popperian and verificationist concepts of causal inference are summarized. Lastly, a recent controversy on meta-analysis is explained. Causal inference plays an important role in epidemiologic theory and medicine. However, because this concept has not been well-introduced in Japan, there has been much misuse of the concept, especially when used for conventional criteria.

Causality↗
PubMed1995

[Models of causal inference: critical analysis of the use of statistics in epidemiology].

The foundations on which the concept of risk has been constructed are discussed. A description of Rubin's model of causal inference, which was first developed in the domain of applied statistics, and later incorporated into a branch of epidemiology, is taken as the starting point. Analysis of the premisses of causal inference brings to light the logical stages in the construction of the concept of risk, allowing it to be understood "from the inside". The abovementioned branch of statistics and epidemiology seeks to demonstrate that statistics can infer causality instead of simply revealing statistical associations; the model gives the basis for estimating that which way be defined as the effect of a cause. Using this procedural distinction between causal inference and association, the model also seeks to differentiate between the epidemiologial dimension of concepts and the merely statistical dimension. This leads to greater complexity when handing the concepts of interation and coofounding. The redective aspects inherent in this methodological construction of risk are here high lighted. Thus, whether applied to individual or populational inferences, this methodological construction imposes limits that need to be taken into account in its theoretical and practical application to epidemiology.

Causality↗
PubMed2006

Two Surgeon General's reports on smoking and cancer: a historical investigation of the practice of causal inference.

BACKGROUND: The epidemiologic literature is replete with conceptual discussions about causal inference, but little is known about how the causal criteria are applied in public health practice. The criteria for causal inference in use today by epidemiologists have been shaped substantially by their use over time in reports of the U.S. Surgeon General on Smoking and Health. METHODS: We reviewed two classic reports on smoking and health from expert committees convened by the US Surgeon General, in 1964 and 1982, in order to evaluate and contrast how the committees applied causal criteria to the available evidence for the different cancer sites at different time periods. We focus on the evidence for four cancer sites in particular that received detailed reviews in the reports: lung, larynx, esophagus and bladder. RESULTS: We found that strength of association and coherence (especially dose-response, biological plausibility and epidemiologic sense) appeared to carry the most weight; consistency carried less weight, and temporality and specificity were apparently not applied at all in some cases. No causal claim was made for associations with a summary odds ratio of less than 3.0. CONCLUSION: Our findings suggest that the causal criteria as described in textbooks and the Surgeon General reports can have variable interpretations and applications in practice. While the authors of these reports may have considered evidential factors that they did not explicitly cite, such lack of transparency of methods undermines the purpose of the causal criteria to promote objective, evidence-based decision making. Further empirical study and critical examination of the process by which causal conclusions are reached can play an important role in advancing the practice of epidemiology by helping public health scientists to better understand the practice of causal inference.

Journal Article↗
PubMed2006

Causal inference in primary open angle glaucoma: specific discussion on intraocular pressure.

OBJECTIVES: As the first part of a comprehensive review of the concept of causal inference in epidemiology, this article explains how causal inference is established in epidemiology and applies these methods to evaluate the evidence in favor or against the causal association between intraocular pressure (IOP) and primary open angle glaucoma (POAG) as an example. METHODS: After an introduction to causal inference in epidemiology and general guidelines for assessment of the causal relationship, the association between IOP and POAG will be evaluated in the context of these guidelines and the categories suggested by the Surgeon General's report. RESULTS: The literature indicates a consistent strong association between IOP and POAG and there is no strong evidence against temporal precedence. The association is biologically plausible, coherent with scientific principles, and has noticeable biological gradient. However, it seems that IOP is not specific for POAG and vice versa. CONCLUSION: Despite the absence of specificity, we conclude that the evidence is sufficient to infer a causal relationship between IOP and POAG. In sum, based on the literature and our current knowledge of glaucoma, currently most competing causes are not strong enough to confront the causal role of IOP.

Causality↗
PubMed1990

Randomization, statistics, and causal inference.

This paper reviews the role of statistics in causal inference. Special attention is given to the need for randomization to justify causal inferences from conventional statistics, and the need for random sampling to justify descriptive inferences. In most epidemiologic studies, randomization and random sampling play little or no role in the assembly of study cohorts. I therefore conclude that probabilistic interpretations of conventional statistics are rarely justified, and that such interpretations may encourage misinterpretation of nonrandomized studies. Possible remedies for this problem include deemphasizing inferential statistics in favor of data descriptors, and adopting statistical techniques based on more realistic probability models than those in common use.

Bayes Theorem↗
PubMed2003

Causal inference from randomized trials in social epidemiology.

Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the causal inference problem in social epidemiology, and the potential for causal inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to causal inference even under experimental conditions and explain which causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology.

Asthma↗
PubMed2005

Doubly robust estimation in missing data and causal inference models.

The goal of this article is to construct doubly robust (DR) estimators in ignorable missing data and causal inference models. In a missing data model, an estimator is DR if it remains consistent when either (but not necessarily both) a model for the missingness mechanism or a model for the distribution of the complete data is correctly specified. Because with observational data one can never be sure that either a missingness model or a complete data model is correct, perhaps the best that can be hoped for is to find a DR estimator. DR estimators, in contrast to standard likelihood-based or (nonaugmented) inverse probability-weighted estimators, give the analyst two chances, instead of only one, to make a valid inference. In a causal inference model, an estimator is DR if it remains consistent when either a model for the treatment assignment mechanism or a model for the distribution of the counterfactual data is correctly specified. Because with observational data one can never be sure that a model for the treatment assignment mechanism or a model for the counterfactual data is correct, inference based on DR estimators should improve upon previous approaches. Indeed, we present the results of simulation studies which demonstrate that the finite sample performance of DR estimators is as impressive as theory would predict. The proposed method is applied to a cardiovascular clinical trial.

Antidepressive Agents↗
PubMed2005

Dissociating processes supporting causal perception and causal inference in the brain.

An understanding of relations between causes and effects is essential for making sense of the dynamic physical world. It has been argued that this understanding of causality depends on both perceptual and inferential components. To investigate whether causal perception and causal inference rely on common or on distinct processes, the authors tested 2 callosotomy (split-brain) patients and a group of neurologically intact participants. The authors show that the direct perception of causality and the ability to infer causality depend on different hemispheres of the divided brain. This finding implies that understanding causality is not a unitary process and that causal perception and causal inference can proceed independently.

Chi-Square Distribution↗
PubMed1995

Causal inference in infectious diseases.

Since the 1970s, Rubin has promoted a model for causal inference based on the potential outcomes if individuals received each of the treatments under study. Commonly, the assumption is made that the outcome in one individual is independent of the treatment assignment and outcome in other individuals. In infectious diseases, however, whether one person become infected is quite often dependent on the infection outcome in other individuals, a situation known as dependent happenings. Here, we review the model proposed by Rubin for the example of infectious disease. Consequences of the violation of the stability assumption include the need for an expanded representation of outcomes, and the existence of different kinds of effects, such as direct and indirect effects. Effects of interest include changes in susceptibility as well as changes in infectiousness. We define the transmission probability formally as an average causal parameter of effect in a population by conditioning on exposure to infection. Unconditional indirect and total effects are difficult to define formally using this model for causal inference. The assignment mechanism can influence the sampling mechanism when it determines who is exposed to infection, raising problems that require further inquiry. We conclude by contrasting the role of differential exposure to infection in direct and indirect effects.

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↗