Probability Of Causation Research Articles

Probability of Causation with Sample Selection: A Reanalysis of the Impacts of Jóvenes en Acción on Formality

This article identifies the probability of causation when there is sample selection. We show that the probability of causation is partially identified for individuals who are always observed regardless of treatment status and derive sharp bounds under three increasingly restrictive sets of assumptions. The first set imposes an exogenous treatment and a monotone sample selection mechanism. To tighten these bounds, the second set also imposes the monotone treatment response assumption, while the third set additionally imposes a stochastic dominance assumption. Finally, we use experimental data from the Colombian job training program Jóvenes en Acción to empirically illustrate our approach’s usefulness. We find that, among always-employed women, at least 10.2% and at most 13.4% transitioned to the formal labor market because of the program. However, our 90%-confidence region does not reject the null hypothesis that the lower bound is equal to zero.

Task-Oriented Robot Cognitive Manipulation Planning Using Affordance Segmentation and Logic Reasoning.

The purpose of task-oriented robot cognitive manipulation planning is to enable robots to select appropriate actions to manipulate appropriate parts of an object according to different tasks, so as to complete the human-like task execution. This ability is crucial for robots to understand how to manipulate and grasp objects under given tasks. This article proposes a task-oriented robot cognitive manipulation planning method using affordance segmentation and logic reasoning, which can provide robots with semantic reasoning skills about the most appropriate parts of the object to be manipulated and oriented by tasks. Object affordance can be obtained by constructing a convolutional neural network based on the attention mechanism. In view of the diversity of service tasks and objects in service environments, object/task ontologies are constructed to realize the management of objects and tasks, and the object-task affordances are established through causal probability logic. On this basis, the Dempster-Shafer theory is used to design a robot cognitive manipulation planning framework, which can reason manipulation regions' configuration for the intended task. The experimental results demonstrate that our proposed method can effectively improve the cognitive manipulation ability of robots and make robots preform various tasks more intelligently.

Finemap-MiXeR: A variational Bayesian approach for genetic finemapping.

Genome-wide association studies (GWAS) implicate broad genomic loci containing clusters of highly correlated genetic variants. Finemapping techniques can select and prioritize variants within each GWAS locus which are more likely to have a functional influence on the trait. Here, we present a novel method, Finemap-MiXeR, for finemapping causal variants from GWAS summary statistics, controlling for correlation among variants due to linkage disequilibrium. Our method is based on a variational Bayesian approach and direct optimization of the Evidence Lower Bound (ELBO) of the likelihood function derived from the MiXeR model. After obtaining the analytical expression for ELBO's gradient, we apply Adaptive Moment Estimation (ADAM) algorithm for optimization, allowing us to obtain the posterior causal probability of each variant. Using these posterior causal probabilities, we validated Finemap-MiXeR across a wide range of scenarios using both synthetic data, and real data on height from the UK Biobank. Comparison of Finemap-MiXeR with two existing methods, FINEMAP and SuSiE RSS, demonstrated similar or improved accuracy. Furthermore, our method is computationally efficient in several aspects. For example, unlike many other methods in the literature, its computational complexity does not increase with the number of true causal variants in a locus and it does not require any matrix inversion operation. The mathematical framework of Finemap-MiXeR is flexible and may also be applied to other problems including cross-trait and cross-ancestry finemapping.

A UK framework for the assessment and integration of different scientific evidence streams in chemical risk assessment

BackgroundFew methods are available for transparently combining different evidence streams for chemical risk assessment to reach an integrated conclusion on the probability of causation. Hence, the UK Committees on Toxicity (COT) and on Carcinogenicity (COC) have reviewed current practice and developed guidance on how to achieve this in a transparent manner, using graphical visualisation. Methods/approachAll lines of evidence, including toxicological, epidemiological, new approach methodologies, and mode of action should be considered, taking account of their strengths/weaknesses in their relative weighting towards a conclusion on the probability of causation. A qualitative estimate of the probability of causation is plotted for each line of evidence and a combined estimate provided. Discussion/conclusionsGuidance is provided on integration of multiple lines of evidence for causation, based on current best practice. Qualitative estimates of probability for each line of evidence are plotted graphically. This ensures a deliberative, consensus conclusion on likelihood of causation is reached. It also ensures clear communication of the influence of the different lines of evidence on the overall conclusion on causality. Issues on which advice from the respective Committees is sought varies considerably, hence the guidance is designed to be sufficiently flexible to meet this need.

Probabilities of Causation with Nonbinary Treatment and Effect

Probabilities of causation are proven to be critical in modern decision-making. This paper deals with the problem of estimating the probabilities of causation when treatment and effect are not binary. Pearl defined the binary probabilities of causation, such as the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN). Tian and Pearl then derived sharp bounds for these probabilities of causation using experimental and observational data. In this paper, we define and provide theoretical bounds for all types of probabilities of causation with multivalued treatments and effects. We further discuss examples where our bounds guide practical decisions and use simulation studies to evaluate how informative the bounds are for various data combinations.

A no-fault risk compensation approach for radiation risks incurred in space travel

In this paper we recommend an appropriate compensation approach should be established for fatality and disabilities that may occur due to space radiation exposures of government or industry workers. A brief review of compensation approaches for nuclear energy and nuclear weapons development workers in the United States and other countries is described. We then summarize issues in the application of probability of causation calculation and provide examples of probability of causation (PC) calculations for missions to the International Space Station and Earth's moon or for Mars exploration. The main focus of this paper follows with a recommendation of a no-fault approach to compensation with the creation of appropriate insurance policies funded by employers to cover all disabilities or fatality, without requiring proof of causation or restriction to conditions that imply causation. Importantly we propose that the compensation described should be managed by recourse to private insurers.

Genome-wide association analysis of plasma lipidome identifies 495 genetic associations

The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.

Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network.

A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach. Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes. Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively. These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.

Causal Confirmation Measures: From Simpson's Paradox to COVID-19.

When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson's Paradox. The Existing Causal Inference Theory (ECIT) can make the overall conclusion consistent with the grouping conclusion by removing the confounder's influence to eliminate the paradox. The ECIT uses relative risk difference Pd = max(0, (R - 1)/R) (R denotes the risk ratio) as the probability of causation. In contrast, Philosopher Fitelson uses confirmation measure D (posterior probability minus prior probability) to measure the strength of causation. Fitelson concludes that from the perspective of Bayesian confirmation, we should directly accept the overall conclusion without considering the paradox. The author proposed a Bayesian confirmation measure b* similar to Pd before. To overcome the contradiction between the ECIT and Bayesian confirmation, the author uses the semantic information method with the minimum cross-entropy criterion to deduce causal confirmation measure Cc = (R - 1)/max(R, 1). Cc is like Pd but has normalizing property (between -1 and 1) and cause symmetry. It especially fits cases where a cause restrains an outcome, such as the COVID-19 vaccine controlling the infection. Some examples (about kidney stone treatments and COVID-19) reveal that Pd and Cc are more reasonable than D; Cc is more useful than Pd.

The relationship of the clinicopathological characteristics and treatment results of post-Chornobyl papillary thyroid microcarcinomas with the latency period and radiation exposure.

A worldwide increase in the incidence of thyroid cancer during the last decades is largely due to papillary thyroid microcarcinomas (MPTCs), which are mostly low-risk tumors. In view of recent clinical recommendations to reduce the extent of surgery for low-risk thyroid cancer, and persisting uncertainty about the impact of radiation history, we set out to address whether clinicopathological characteristics and prognosis of post-Chornobyl MPTCs were changing with regard to: i) the latency period, ii) probability of causation (POC) of a tumor due to radiation, and iii) tumor size. Patients (n = 465) aged up to 50 years at diagnosis who lived in April, 1986 in six northern, most radiocontaminated regions of Ukraine were studied. Latency period was statistically significantly associated with the reduction of POC level, tumor size and the frequency of fully encapsulated MPTCs. In contrast, the frequency of oncocytic changes and the BRAFV600E mutation increased. Invasive properties and clinical follow-up results did not depend on latency except for a lower frequency of complete remission after postsurgical radioiodine therapy. The POC level was associated with more frequent extrathyroidal extension, and lymphatic/vascular invasion, less frequent oncocytic changes and BRAFV600E , and did not associate with any clinical indicator. Tumor size was negatively associated with the latency period and BRAFV600E , and had a statistically significant effect on invasive properties of MPTCs: both the integrative invasiveness score and its components such as lymphatic/vascular invasion, extrathyroidal extension and lymph node metastases increased. The frequency of total thyroidectomy, neck lymph node dissection and radioiodine therapy also increased with the larger tumor size. The duration of the latency period, POC level or tumor size did not associate with the chance of disease recurrence. In summary, we did not observe overall worsening of the clinicopathological features or treatment results of radiogenic MPTCs that could be associated with the latency period or POC level, suggesting that radiation history did not strongly affect those in the analyzed MPTC patients. However, the increase in the invasive properties with tumor size indicates the need for individual risk stratification for each MPTC patient, regardless of radiation history, for treatment decision-making.

Multiple Myeloma in Radiography Testing Worker

Backgrounds: Radiography Testing (RT) is a non-destructive testing method mainly using x-ray and γ-rays emitted from Ir192 and Co60 nuclides. X-rays, and γ-rays are reported by International Agency for Research on Cancer (IARC) as carcinogens inducing multiple myeloma in human with limited evidences. In this study, the amount of exposure to radiation and probability of causation (PC) of multiple myeloma were assessed in a 65-year-old of patient who worked as an inspector in shipyards and plumbing works using radiation for 9 years. Methods: Cumulative radiation exposure was calculated using the records of past personal dosimeter and previous studies. PC developed by the Occupational Safety and Health Research Institute (OSHRI) and Radiation Health Research Institute was used to assess the causal relationship between radiation exposure and disease under exposure scenarios. Results: Considering that the patient rarely used the collimator nor shielding equipment during works at shipyard (1983~1988), we calculated the radiation exposure under the scenario assuming collimator was used in half at shipyard work. The radiation exposure dose of workers was estimated to be a minimum of 73.76mSv and a maximum of 1688.34mSv. The PC accounting for attribution of radiation to the cancer was 47.74% applying the upper 95th confidence value. Conclusions: The work-relatedness between RT without using protective equipment and shielding in the past and multiple myeloma was approved by the Epidemiological Investigation and Evaluation Committee for this case. Keywords: Occupational disease, Multiple myeloma, Radiography testing, Radiation exposure dose, KOSHA-PEPC

Novel Method for Early Prediction of Clinically Significant Drug-Drug Interactions with a Machine Learning Algorithm Based on Risk Matrix Analysis in the NICU.

Aims: Evidence for drug–drug interactions (DDIs) that may cause age-dependent differences in the incidence and severity of adverse drug reactions (ADRs) in newborns is sparse. We aimed to develop machine learning (ML) algorithms that predict DDI presence by integrating each DDI, which is objectively evaluated with the scales in a risk matrix (probability + severity). Methods: This double-center, prospective randomized cohort study included neonates admitted to the neonatal intensive care unit in a tertiary referral hospital during the 17-month study period. Drugs were classified by the Anatomical Therapeutic Chemical (ATC) classification and assessed for potential and clinically relevant DDIs to risk analyses with the Drug Interaction Probability Scale (DIPS, causal probability) and the Lexicomp® DDI (severity) database. Results: A total of 412 neonates (median (interquartile range) gestational age of 37 (4) weeks) were included with 32,925 patient days, 131 different medications, and 11,908 medication orders. Overall, at least one potential DDI was observed in 125 (30.4%) of the patients (2.6 potential DDI/patient). A total of 38 of these 125 patients had clinically relevant DDIs causing adverse drug reactions (2.0 clinical DDI/patient). The vast majority of these DDIs (90.66%) were assessed to be at moderate risk. The performance of the ML algorithms that predicts of the presence of relevant DDI was as follows: accuracy 0.944 (95% CI 0.888–0.972), sensitivity 0.892 (95% CI 0.769–0.962), F1 score 0.904, and AUC 0.929 (95% CI 0.874–0.983). Conclusions: In clinical practice, it is expected that optimization in treatment can be achieved with the implementation of this high-performance web tool, created to predict DDIs before they occur with a newborn-centered approach.

Leveraging Systematic Reviews to Explore Disease Burden and Costs of Per- and Polyfluoroalkyl Substance Exposures in the United States

Accelerating evidence confirms the contribution of per- and polyfluoroalkyl substances (PFAS) to disease burden and disability across the lifespan. Given that policy makers raise the high cost of remediation and of substituting PFAS with safer alternatives in consumer products as barriers to confronting adverse health outcomes associated with PFAS exposure, it is important to document the costs of inaction even in the presence of uncertainty. We therefore quantified disease burdens and related economic costs due to legacy PFAS exposures in the US in 2018. We leveraged systematic reviews and used meta-analytic inputs whenever possible, identified previously published exposure–response relationships, and calculated PFOA- and PFOS-attributable increases in 13 conditions. These increments were then applied to census data to determine total annual PFOA- and PFOS-attributable cases of disease, from which we calculated economic costs due to medical care and lost productivity using previously published cost-of-illness data. We identified PFAS-attributable disease costs in the US of $5.52 billion across five primary disease endpoints shown to be associated with PFAS exposure in meta-analyses. This estimate represented the lower bound, with sensitivity analyses revealing as much as $62.6 billion in overall costs. While further work is needed to assess probability of causation and establish with greater certainty effects of the broader category of PFAS, the results confirm further that public health and policy interventions are still necessary to reduce exposure to PFOA and PFOS and their endocrine-disrupting effects. This study demonstrates the large potential economic implications of regulatory inaction.

Evaluating causes of effects by posterior effects of causes

SummaryFor the case with a single causal variable, Dawid et al. (2014) defined the probability of causation, and Pearl (2000) defined the probability of necessity to assess the causes of effects. For a case with multiple causes that could affect each other, this paper defines the posterior total and direct causal effects based on the evidence observed for post-treatment variables, which could be viewed as measurements of causes of effects. Posterior causal effects involve the probabilities of counterfactual variables. Thus, as with the probability of causation, the probability of necessity and direct causal effects, the identifiability of posterior total and direct causal effects requires more assumptions than the identifiability of traditional causal effects conditional on pre-treatment variables. We present assumptions required for the identifiability of posterior causal effects and provide identification equations. Further, when the causal relationships between multiple causes and an endpoint can be depicted by causal networks, we can simplify both the required assumptions and the identification equations of the posterior total and direct causal effects. Finally, using numerical examples, we compare the posterior total and direct causal effects with other measures for evaluating the causes of effects and the population attributable risks.

Clinicopathological Implications of the BRAF V600E Mutation in Papillary Thyroid Carcinoma of Ukrainian Patients Exposed to the Chernobyl Radiation in Childhood: A Study for 30 Years After the Accident.

With time after the Chernobyl accident, the number of papillary thyroid carcinomas (PTCs) driven by the BRAFV600E oncoprotein is growing in patients exposed to radiation at a young age. Clinicopathological associations of BRAFV600E in PTCs from patients with internal radiation history have not been sufficiently studied so far. This work analyzes the structural characteristics, proliferative activity, invasive features, clinical information, and dosimetric data in the BRAFV600E-positive and BRAFV600E-negative PTCs from the Ukrainian patients exposed to Chernobyl radiation and treated over 30 years after the accident. The study included 428 PTCs from patients aged 4–49 years at surgery who lived in the six northern regions of Ukraine most contaminated by 131I, were ≤18 years of age at the time of exposure, and were operated on from 1990 to 2017. Immunohistochemical staining for BRAFV600E was performed with the VE1 antibody. The probability of causation (POC) of a tumor due to radiation was determined using an interactive online NIH/NCI software. BRAFV600E was detected in 136/428 (31.8%) PTCs. In comparison with the BRAFV600E-negative PTCs, the BRAFV600E-positivity was associated with older patient age at the accident and at surgery, a longer period of latency, and lower POC. The BRAFV600E-positive PTCs were characterized by smaller tumor size, higher Ki67 labeling index, more frequent oncocytic changes, multifocality, and dominant papillary growth pattern. Tumor invasive features were less frequent in the BRAFV600E-positive PTCs and did not change with POC level. Despite a less aggressive tumor phenotype, BRAFV600E was a risk factor for recurrence, namely radioiodine-refractory (RAI-R) recurrent metastases. Multivariate models of RAI-R included BRAFV600E and/or histopathological parameters closely correlating with BRAFV600E such as tumor size, multifocality, dominant papillary growth pattern, or oncocytic changes. Thus, the BRAFV600E-positive PTCs from patients from a high-risk group for radiogenic thyroid cancer diagnosed in the 30 years after the Chernobyl accident did not display higher invasiveness regardless of POC level, but in view of the prognostic impact of this genetic alteration, knowledge of the BRAF status may be beneficial for middle-aged patients with radiogenic PTC considered for RAI therapy, and suggests more careful follow-up of patients with the BRAFV600E-positive tumors.

Automated causal inference in application to randomized controlled clinical trials

Randomized controlled trials (RCTs) are considered the gold standard for testing causal hypotheses in the clinical domain; however, the investigation of prognostic variables of patient outcome in a hypothesized cause–effect route is not feasible using standard statistical methods. Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data. Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remains consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis.

Learning Disease Causality Knowledge From the Web of Health Data

Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies together. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.

Indoor exposure to phthalates and its burden of disease in China.

China's profoundly rapid modernization in the past two decades has resulted in dramatic changes in indoor environmental exposures. Among these changes, exposure to phthalates has attracted increasing attention. We aimed to characterize indoor phthalate exposure and to estimate the disease burden attributable to indoor phthalate pollution from 2000 to 2017 in China. We integrated the national exposure level of indoor phthalates from literature through systematic review and Monte Carlo simulation. Dose-response relationships between phthalate exposure and health outcomes were obtained by systematic review and meta-analysis. Based on existing models for assessing probabilities of causation and a comprehensive review of available data, we calculated the disability-adjusted life years (DALYs) among the general Chinese population resulting from exposure to indoor phthalate pollution. We found that DnBP, DiBP, and DEHP were the most abundant phthalates in indoor environments of residences, offices, and schools with medians of national dust phase concentration from 74.5µg/g to 96.3µg/g, 39.6µg/g to 162.5µg/g, 634.2µg/g to 1,394.7µg/g, respectively. The national equivalent exposure for children to phthalates in settled dust was higher than that of adults except for DiBP and DnOP. Dose-response relationships associated with DEP, DiBP, DnBP, BBzP, and DEHP exposures were established. Between 2000 and 2017, indoor phthalate exposure in China has led to 3.32million DALYs per year, accounting for 0.90% of total DALYs across China. The annual DALY associated with indoor phthalate pollution in China was over 2000 people per million, which is about 2~3 times of the DALY loss due to secondhand smoke (SHS) in six European countries or the sum of the DALY loss caused by indoor radon and formaldehyde in American homes. Our study indicates a considerable socioeconomic impact of indoor phthalate exposure for a modernizing human society. This suggest the need for relevant national standard and actions to reduce indoor phthalate exposure.

Research on the Graphical Model Structure Characteristic of Strong Exogeneity Based on Twin Network Method and Its Application in Causal Inference

Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the actual world and the hypothetical world, which facilitates the investigating of strong exogeneity. In this paper, the graphical model structure characteristic of strong exogeneity is investigated based on the twin network method. Compared with other derivation methods of graphical diagnosis, the method based on the twin network is more concise, clearer, and easier to understand. Under the condition of strong exogeneity, it is easy to estimate the probability of causation based on observational data. As an example, the application of graphical model structure characteristic of strong exogeneity in causal inference in the context of lung cancer simple sets (LUCAS) is illustrated.

Bounding Causes of Effects With Mediators

Suppose X and Y are binary exposure and outcome variables, and we have full knowledge of the distribution of Y, given application of X. We are interested in assessing whether an outcome in some case is due to the exposure. This “probability of causation” is of interest in comparative historical analysis where scholars use process tracing approaches to learn about causes of outcomes for single units by observing events along a causal path. The probability of causation is typically not identified, but bounds can be placed on it. Here, we provide a full characterization of the bounds that can be achieved in the ideal case that X and Y are connected by a causal chain of complete mediators, and we know the probabilistic structure of the full chain. Our results are largely negative. We show that, even in these very favorable conditions, the gains from positive evidence on mediators is modest.