Probability Of Causation Research Articles

A Non-Parametric Projection-Based Estimator for the Probability of Causation, with Application to Water Sanitation in Kenya

SummaryCurrent estimation methods for the probability of causation ‘PC’ make strong parametric assumptions or are inefficient. We derive a non-parametric influence-function-based estimator for a projection of PC, which allows for simple interpretation and valid inference by making weak structural assumptions. We apply our estimator to real data from an experiment in Kenya. This experiment found, by estimating the average treatment effect, that protecting water springs reduces childhood disease. However, before scaling up this intervention, it is important to determine whether it was the exposure, and not something else, that caused the outcome. Indeed, we find that some children, who were exposed to a high concentration of bacteria in drinking water and had a diarrhoeal disease, would probably have contracted the disease absent the exposure since the estimated PC for an average child in this study is 0.12 with a 95% confidence interval of (0.11, 0.13). Our non-parametric method offers researchers a way to estimate PC, which is essential if we wish to determine not only the average treatment effect, but also whether an exposure probably caused the observed outcome.

A work-relatedness assessment in epidemiological case investigation of occupational cancers: I. Principles.

It is important to identify the causal relationship between occupational exposure and the disease in the area of compensation because most cancers have long latent period. This review presents the principles that should be considered when evaluating the work-relatedness. We reviewed reports on occupational cancers published by the International Agency for Research on Cancer, American Conference of Governmental Industrial Hygienists, National Toxicology Program, Environmental Protection Agency, European Union, which are the world's most prestigious organizations. In addition, we reviewed relevant papers and books published in Korea. The process is conducted in the order of cancer diagnosis, exposure assessment for carcinogens, and work-relatedness assessment. The probability of causation is determined stochastically rather than deterministically. There is no absolute standard for accreditation criteria and results may vary according to expert opinions and in each country or organization. Revealing the causal relationship of occupational cancers is a difficult process owing to inconsistency in relevant epidemiological studies, lack of well-established biological mechanisms, loss of objective occupational historical data, and other complexity of individual cases. Causation is unclear in many cases. Nevertheless, the data should be reviewed in detail for each case suspected of work-related cancers, and an assessment should be made through valid and plausible logic and literature evidences.

Causal decision theory’s predetermination problem

It has often been noted that there is some tension between engaging in decision-making and believing that one’s choices might be predetermined. The possibility that our choices are predetermined forces us to consider, in our decisions, act-state pairs which are inconsistent, and hence to which we cannot assign sensible utilities. But the reasoning which justifies two-boxing in Newcomb’s problem also justifies associating a non-zero causal probability with these inconsistent act-state pairs. Put together these undefined utilities and non-zero probabilities entail that expected utilities are undefined whenever it is a possibility that our choices are predetermined. There are three ways to solve the problem, but all of them suffer serious costs: always assume that, contrary to our evidence, the outcome of our present decision-making is not predetermined; give up the reasoning that justifies unconditional two-boxing in Newcomb’s problem; or allow epistemically impossible outcomes to contribute to expected utility, leading to the wrong results in a series of cases introduced by Ahmed (Br J Philos Sci 65(4):665–685, 2014a, Evidence, decision and causality, Cambridge University Press, Cambridge, 2014b). However they choose to respond, causal decision theorists cannot remain silent: the intuitive tension between decision-making and the possibility of predetermination can be made precise, and resolving it will require giving up something. Causal decision theorists have a predetermination problem.

Disease burden attributable to endocrine-disrupting chemicals exposure in China: A case study of phthalates

While it has been acknowledged that exposure to endocrine-disrupting chemicals (EDCs) is associated with human diseases, the overall disease burden attributable to the exposure to a specific EDC has rarely been evaluated. Based on existing models for assessing probabilities of causation and a comprehensive review of available data, we analyzed the burden of three diseases, i.e., male infertility, adult obesity, and diabetes, among the general Chinese population resulting from exposure to phthalates. Our estimation indicates that exposure to phthalates is associated with ~2.50 million cases of the three diseases across China in 2010, causing ~57.2 billion Chinese Yuan (equivalent to ~9 billion US dollars) of health care costs in a year. Male infertility has the largest number of cases, followed by adult obesity and diabetes. Based on these phthalate-specific estimates, we further estimated that the total disease cost due to exposure to the overall EDCs amounted to ~429.43 billion Chinese Yuan in China in 2010, accounting for 1.07% of nationwide gross domestic product (GDP). When comparing our results with an earlier estimate for the European Union (EU) member countries, we find that exposure to phthalates leads to quite a similar disease burden per unit of GDP in both regions. Our study illustrates the considerable socio-economic impact of EDC exposure on human society, implying the imperative need for global risk reduction actions on EDCs, especially in view of the 2030 Sustainable Development Goals.

Work-Relatedness.

Work-Relatedness.

Organ at risk dose measurements following radiotherapy treatment for breast cancer patients

Radiotherapy technologies are one of the most common treatments for cancer, they have been successfully used to damage cancer cells, with as little harm as possible to nearby healthy cells, however Secondary cancer risk following radiotherapy is an increasingly important topic in clinical oncology with impact on treatment decision making and on patient management. The objective of this study is to measure the skin radiation dose received by the treated breast as well as the dose to the lungs in cancer patients undergoing breast radiotherapy and estimate the probability for radiation-induced cancer, i.e. secondary cancer and the probability of lung cancer. Usage of RANDO phantoms and GafChromic dosimetry films to do dose measurements during breast radiotherapy. Measured dose and calculated organ dose from patients were compared. The calculated doses were done using the effective dose formula. Measured point doses of both lungs were compared to those calculated by TPS (treatment planning system). Relative Risk (RR) and Probability of Causation (PC) of lungs- probability of cancer- were calculated from patient’s mean doses to the organs (right lung and left lung) using the model described in BEIR V report. RR mean values were 1.278 and 1.032 for ipsilateral and contralateral lungs respectively. In addition, PC mean values were 20.93 % and 3.099% for ipsilateral and contralateral lungs respectively. Breast cancer patients are susceptible to an increased risk of secondary cancer of ipsilateral lungs more than in the contralateral ones.

The probability of external causation: An empirical account of crosslinguistic variation in lexical causatives

AbstractThis corpus-driven computational study addresses the question of why some verbs in some languages participate in the causative alternation while their counterparts in other languages do not. The results of this study suggest that the lexical property that underlies this variation is the probability of external causation. Alternating verbs are distributed on a scale of increasing probability for an external causer to occur. The probability of external causation can be empirically assessed in two ways, among others: first, by observing the typological distribution of causative and anticausative morphological markings across a wide range of languages; second, through the frequency distribution of causative and anticausative uses of the alternating verbs in a corpus of a single language. Our study reveals that these two measures are correlated. Moreover, we demonstrate that the corpus-based measure is applicable to a wide range of verbs. Extending the corpus-based investigation comparatively across two languages, English and German, we find that the frequencies of crosslinguistic realizations of lexical causatives are modulated by the probability of external causation, an underlying parameter assigned to verb types. Finally, we propose a probabilistic graphical model that clusters verbs based on the relation between the crosslinguistic distribution of their causative and anticausative realizations and the probability of external causation.

Causal Reasoning and Data Analysis in the Law: Definition, Estimation, and Usage of the Probability of Causation

Researchers often need to determine whether a specific exposure, or something else, caused an individual’s outcome. To answer questions of causality in which the exposure and outcome have already been observed, researchers have suggested estimating the probability of causation (PC). However, authors disagree about the proper definitions and identification strategies for PC, and current estimation methods for PC make strong parametric assumptions, or are inefficient and do not easily yield inferential tools. I discuss the definition and identification assumptions of PC, derive a novel estimation method for PC under standard identifiability assumptions under which PC equals the excess risk ratio, and derive an influence-function-based nonparametric estimator for PC, which allows for simple interpretation and valid inference by making only weak structural assumptions. Finally, I present an application of my estimation method.

Causality diagram-based scheduling approach for blast furnace gas system

Rational use of blast furnace gas U+0028 BFG U+0029 in steel industry can raise economic profit, save fossil energy resources and alleviate the environment pollution. In this paper, a causality diagram is established to describe the causal relationships among the decision objective and the variables of the scheduling process for the industrial system, based on which the total scheduling amount of the BFG system can be computed by using a causal fuzzy C-means U+0028 CFCM U+0029 clustering algorithm. In this algorithm, not only the distances among the historical samples but also the effects of different solutions on the gas tank level are considered. The scheduling solution can be determined based on the proposed causal probability of the causality diagram calculated by the total amount and the conditions of the adjustable units. The causal probability quantifies the impact of different allocation schemes of the total scheduling amount on the BFG system. An evaluation method is then proposed to evaluate the effectiveness of the scheduling solutions. The experiments by using the practical data coming from a steel plant in China indicate that the proposed approach can effectively improve the scheduling accuracy and reduce the gas diffusion.

Probability of causation for occupational cancer after exposure to ionizing radiation

BackgroundProbability of causation (PC) is a reasonable way to estimate causal relationships in radiation-related cancer. This study reviewed the international trend, usage, and critiques of the PC method. Because it has been used in Korea, it is important to check the present status and estimation of PC in radiation-related cancers in Korea.MethodsResearch articles and official reports regarding PC of radiation-related cancer and published from the 1980s onwards were reviewed, including studies used for the revision of the Korean PC program. PC has been calculated for compensation-related cases in Korea since 2005.ResultsThe United States National Institutes of Health first estimated the PC in 1985. Among the 106 occupational diseases listed in the International Labor Organization Recommendation 194 (International Labor Office (ILO), ILO List of Occupational Diseases, 2010), PC is available only for occupational cancer after ionizing radiation exposure. The United States and United Kingdom use PC as specific criteria for decisions on the compensability of workers’ radiation-related health effects. In Korea, PC was developed firstly as Korean Radiation Risk and Assigned Share (KORRAS) in 1999. In 2015, the Occupational Safety and Health Research Institute and Radiation Health Research Institute jointly developed a more revised PC program, Occupational Safety and Health-PC (OSH-PC). Between 2005 and 2015, PC was applied in 16 claims of workers’ compensation for radiation-related cancers. In most of the cases, compensation was given when the PC was more than 50%. However, in one case, lower than 50% PC was accepted considering the possibility of underestimation of the cumulative exposure dose.ConclusionsPC is one of the most advanced tools for estimating the causation of occupational cancer. PC has been adjusted for baseline cancer incidence in Korean workers, and for uncertainties using a statistical method. Because the fundamental reason for under- or over-estimation is probably inaccurate dose reconstruction, a proper guideline is necessary.

Probabilities of Causation of Climate Changes

Multiple changes in Earth’s climate system have been observed over the past decades. Determining how likely each of these changes is to have been caused by human influence is important for decision making with regard to mitigation and adaptation policy. Here we describe an approach for deriving the probability that anthropogenic forcings have caused a given observed change. The proposed approach is anchored into causal counterfactual theory ( Pearl 2009 ), which was introduced recently, and in fact partly used already, in the context of extreme weather event attribution (EA). We argue that these concepts are also relevant to, and can be straightforwardly extended to, the context of detection and attribution of long-term trends associated with climate change (D&A). For this purpose, and in agreement with the principle of fingerprinting applied in the conventional D&A framework, a trajectory of change is converted into an event occurrence defined by maximizing the causal evidence associated to the forcing under scrutiny. Other key assumptions used in the conventional D&A framework, in particular those related to numerical model error, can also be adapted conveniently to this approach. Our proposal thus allows us to bridge the conventional framework with the standard causal theory, in an attempt to improve the quantification of causal probabilities. An illustration suggests that our approach is prone to yield a significantly higher estimate of the probability that anthropogenic forcings have caused the observed temperature change, thus supporting more assertive causal claims.

A Nonparametric Projection-Based Estimator for the Probability of Causation, with Application to Water Sanitation in Kenya

Current estimation methods for the probability of causation (PC) make strong parametric assumptions or are inefficient. We derive a nonparametric influence-function-based estimator for a projection of PC, which allows for simple interpretation and valid inference by making weak structural assumptions. We apply our estimator to real data from an experiment in Kenya, which found, by estimating the average treatment effect, that protecting water springs reduces childhood disease. However, before scaling up this intervention, it is important to determine whether it was the exposure, and not something else, that caused the outcome. Indeed, we find that some children, who were exposed to a high concentration of bacteria in drinking water and had a diarrheal disease, would likely have contracted the disease absent the exposure since the estimated PC for an average child in this study is 0.12 with a 95% confidence interval of (0.11, 0.13). Our nonparametric method offers researchers a way to estimate PC, which is essential if one wishes to determine not only the average treatment effect, but also whether an exposure likely caused the observed outcome.

The probability of causation1

Abstract Many legal cases require decisions about causality, responsibility or blame, and these may be based on statistical data. However, causal inferences from such data are beset by subtle conceptual and practical difficulties, and in general it is, at best, possible to identify the ‘probability of causation’ as lying between certain empirically informed limits. These limits can be refined and improved if we can obtain additional information, from statistical or scientific data, relating to the internal workings of the causal processes. In this article we review and extend recent work in this area, where additional information may be available on covariate and/or mediating variables.

Preliminary Results of a Novel Algorithmic Method Aiming to Support Initial Causality Assessment of Routine Pharmacovigilance Case Reports for Medication-Induced Liver Injury: The PV-RUCAM.

Data incompleteness in pharmacovigilance (PV) health records limits the use of current causality assessment methods for drug-induced liver injury (DILI). In addition to the inherent complexity of this adverse event, identifying cases of high causal probability is difficult. The aim was to evaluate the performance of an improved, algorithmic and standardised method called the Pharmacovigilance-Roussel Uclaf Causality Assessment Method (PV-RUCAM), to support assessment of suspected DILI. Performance was compared in different settingswith regard to applicability and differentiation capacity. A PV-RUCAM score was developed based on the seven sections contained in the original RUCAM. The score provides cut-off values for or against DILI causality, and was applied on two datasets of bona fide individual case safety reports (ICSRs) extracted randomly from clinical trial reports and a third dataset of electronic health records from a global PV database. The performance of PV-RUCAM adjudication was compared against two standards: a validated causality assessment method (original RUCAM) and global introspection. The findings showed moderate agreement against standards. The overall error margin of no false negatives was satisfactory, with 100% sensitivity, 91% specificity, a 25% positive predictive value and a 100% negative predictive value. The Spearman's rank correlation coefficient illustrated a statistically significant monotonic association between expert adjudication and PV-RUCAM outputs (R=0.93). Finally, there was high inter-rater agreement (K w=0.79) between two PV-RUCAM assessors. Within the PV setting of a pharmaceutical company, the PV-RUCAM has the potential to facilitate and improve the assessment done by non-expert PV professionals compared with other methods when incomplete reports must be evaluated for suspected DILI. Prospective validation of the algorithmic toolis necessary prior to implementation for routine use.

Judgment for cause of radiogenic neoplasms for a thyroid cancer

Background: Radiogenic neoplasms due to occupational exposure can be identified as occupational radiogenic neoplasms. We investigate the etiology of cancer in a patient with thyroid carcinoma. Methods: Organ dose was estimated based on the individual monitoring results, or according to GB/T 16149-2012 “Specification of dose estimation for chronic radiation sickness from external exposure” when no individual dose was provided; PC (probability of causation) was calculated according to GBZ97-2009 “Judgment criteria for cause of radiogenic neoplasms”. Results: The estimated cumulative dose was 20.4 cGy, organ dose of thyroid was 38.1 cGy, PC value from occupational exposure was about 37.8%, with its upper limit of the 95% confidence level was 82.6%. Conclusions: The case can be judged as radiogenic neoplasm as the upper limit of the 95% confidence PC value is more than 50%. More attention should be paid to the examination of thyroid palpation and the thyroid B ultrasound for radiation workers, especially for those high-risk individuals engaged in radiation work in their early years.

Use of a gene-based case-control association approach in exome sequencing data to elucidate the molecular basis of a mendelian phenotype

BackgroundOver the past 5 years, exome sequencing and whole-genome sequencing have been extensively used to identify genes underlying rare mendelian disorders. These techniques have accelerated not only discovery but also false-positive reports of causality. To address this issue, we developed a statistical inference framework that evaluates the strength of findings from such studies. This method was applied to exome sequencing data from individuals with a specific retinal dystrophy, aiming to elucidate the genetic basis of their visual impairment. Methods28 unrelated patients and 1917 controls with no retinal disease underwent exome sequencing. Patients had a progressive retinal dystrophy phenotype with early cone photoreceptor involvement, absence of retinal flecks on fundus autofluorescence imaging, and an unknown molecular diagnosis. Genetic data from cases and controls were analysed with the same bioinformatics pipeline. A gene-based case-control association study was then performed and gene-based p values were derived. FindingsThe initial analysis focused on rare, presumed loss-of-function variants; the most significant binomial p value (p=2 × 10−5) was obtained for TTLL5. Closer inspection highlighted biallelic loss-of-function variants in this gene as a probable cause of the studied retinal dystrophy. A second analysis using a recessive model (presence of ≥2 rare, potentially functional variants) was then performed. The most significant binomial p value (p=1 × 10−4) was obtained for DRAM2, highlighting mutations in this gene as another likely cause of this retinal dystrophy. Validation studies identified additional mutation-positive individuals (four with TTLL5-retinopathy and five with DRAM2-retinopathy in total). Overall, a molecular diagnosis was identified in 15 of the 28 patients. InterpretationUsing a phenotype-driven cluster analysis, we have identified two previously unreported disease-associated genes. The success of our method highlights the key role of precise phenotyping in enhancing the utility of genomic investigations. Additionally, we have described a robust genome-wide statistical framework for objectively assigning probability of causation to new candidate genes and variants. This approach is broadly applicable to the study of rare mendelian disorders. FundingNational Institute for Health Research, Macular Society UK, RP Fighting Blindness, Fight For Sight, National Eye Research Centre, Wellcome Trust.

Towards a New Model for Causal Reasoning in Expert Systems

This paper presents ideas for improved conditional probability assessment and improved expert systems consultations. It cautions that knowledge engineers may sometimes be imprecise when capturing causal information from experts: their elicitation questions may not distinguish between causal and correlational expertise. This paper shows why and how such models cannot support normative inferencing over conditional probabilities as if they were all based on frequencies in the long run. In some cases, these probabilities are instead causal theory-based judgments, and therefore are not traditional conditional probabilities. This paper argues that these should be processed as if they were causal strength probabilities or causal propensity probabilities. This paper reviews the literature on causal and probability judgment, and then presents a probabilistic inferencing model that integrates theory-based causal probabilities with frequency-based conditional probabilities. The paper also proposes guidelines for elicitation questions that knowledge engineers may use to avoid conflating causal theory-based judgment with frequency based judgment.

A Comment on Statistical Significance and Standards of Proof

A Comment on Statistical Significance and Standards of Proof

Risk Evaluation of Secondary Cancer Induced by Breast Cancer Radiotherapy Treatment

The Radiotherapy technologies are one of the most common treatments for cancer, they have been successfully used to damage cancer cells, with as little harm as possible to nearby healthy cells, however Secondary cancer risk following radiotherapy is an increasingly important topic in clinical oncology with impact on treatment decision making and on patient management. The paper aims to investigate and evaluate the current practice in radiotherapy through its different phases and to quantify the risk of secondary primary cancer following radiotherapy in breast cancer by dose calculation and risk estimation of second cancer; to make recommendations for a safer practice in radiotherapy department. The data collected from NBGUH and RHUH with dosimetry calculations on retrospective female patient’s files and verifies with dose measurements on RANDO Phantom.The results showed 85% of compliance in radiotherapy safe practices, calculated Probability of Causation after breast radiotherapy for contralateral and Ipsilateral breast cancer 0.17%, and for lung is 32%, for spinal cord is 24%. While the PC in phantom measurements in Ipsilateral lung 43.22% and PC for contralateral lung 3.099%.

Exposure to endocrine-disrupting chemicals in the USA: a population-based disease burden and cost analysis

Endocrine-disrupting chemicals (EDCs) contribute to disease and dysfunction and incur high associated costs (>1% of the gross domestic product [GDP] in the European Union). Exposure to EDCs varies widely between the USA and Europe because of differences in regulations and, therefore, we aimed to quantify disease burdens and related economic costs to allow comparison. We used existing models for assessing epidemiological and toxicological studies to reach consensus on probabilities of causation for 15 exposure-response relations between substances and disorders. We used Monte Carlo methods to produce realistic probability ranges for costs across the exposure-response relation, taking into account uncertainties. Estimates were made based on population and costs in the USA in 2010. Costs for the European Union were converted to US$ (€1=$1·33). The disease costs of EDCs were much higher in the USA than in Europe ($340 billion [2·33% of GDP] vs $217 billion [1·28%]). The difference was driven mainly by intelligence quotient (IQ) points loss and intellectual disability due to polybrominated diphenyl ethers (11 million IQ points lost and 43 000 cases costing $266 billion in the USA vs 873 000 IQ points lost and 3290 cases costing $12·6 billion in the European Union). Accounting for probability of causation, in the European Union, organophosphate pesticides were the largest contributor to costs associated with EDC exposure ($121 billion), whereas in the USA costs due to pesticides were much lower ($42 billion). EDC exposure in the USA contributes to disease and dysfunction, with annual costs taking up more than 2% of the GDP. Differences from the European Union suggest the need for improved screening for chemical disruption to endocrine systems and proactive prevention. Endocrine Society, Ralph S French Charitable Foundation, and Broad Reach Foundation.