CAUSAL INTERPRETATIONS OF BLACK-BOX MODELS.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhao Q
PROVIDER: S-EPMC7597863 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
CAUSAL INTERPRETATIONS OF BLACK-BOX MODELS.
Zhao Qingyuan Q Hastie Trevor T
Journal of business & economic statistics : a publication of the American Statistical Association 20190705
The fields of machine learning and causal inference have developed many concepts, tools, and theory that are potentially useful for each other. Through exploring the possibility of extracting causal interpretations from black-box machine-trained models, we briefly review the languages and concepts in causal inference that may be interesting to machine learning researchers. We start with the curious observation that Friedman's partial dependence plot has exactly the same formula as Pearl's back-d ...[more]
Similar Datasets
Project description:The basal ganglia (BG) is a brain structure that has long been proposed to play an essential role in action selection, and theoretical models of spiking neurons have tried to explain how the BG solves this problem. A recently proposed functional and biologically inspired network model of the striatum (an important nucleus of the BG) is based on spike-timing-dependent eligibility (STDE) and captured important experimental features of this nucleus. The model can recognize complex input patterns and consistently choose rewarded actions to respond to such sensory inputs. However, model tuning is challenging due to two main reasons. The first is the expert knowledge required, resulting in tedious and potentially biased trial-and-error procedures. The second is the computational cost of assessing model configurations (approximately 1.78 h per evaluation). This study addresses the model tuning problem through numerical optimization. Considering the cost of assessing solutions, the selected methods stand out due to their low requirements for solution evaluations and compatibility with high-performance computing. They are the SurrogateOpt solver of Matlab and the RBFOpt library, both based on radial basis function approximations, and DIRECT-GL, an enhanced version of the widespread black-box optimizer DIRECT. Besides, a parallel random search serves as a baseline reference of the outcome of opting for sophisticated methods. SurrogateOpt turns out to be the best option for tuning this kind of model. It outperforms, on average, the quality of the configuration found by an expert and works significantly faster and autonomously. RBFOpt and the random search share the second position, but their average results are below the option found by hand. Finally, DIRECT-GL follows this line becoming the worst-performing method.
| S-EPMC9630480 | biostudies-literature
Project description:Accounting for the interannual climatic variations is a well-known issue for simulation-based studies of environmental systems. It often requires intensive sampling (e.g., averaging the simulation outputs over many climatic series), which hinders many sequential processes, in particular optimization algorithms. We propose here an approach based on a subset selection in a large basis of climatic series, using an ad-hoc similarity function and clustering. A non-parametric reconstruction technique is introduced to estimate accurately the distribution of the output of interest using only the subset sampling. The proposed strategy is non-intrusive and generic (i.e. transposable to most models with climatic data inputs), and can be combined to most "off-the-shelf" optimization solvers. We apply our approach to sunflower ideotype design using the crop model SUNFLO. The underlying optimization problem is formulated as a multi-objective one to account for risk-aversion. Our approach achieves good performances even for limited computational budgets, outperforming significantly standard strategies.
| S-EPMC5444610 | biostudies-literature
Project description:Software systems typically have an input domain that can be subdivided into sub-domains, each of which generates similar or related outputs. Testing it on the boundaries between these sub-domains is critical to ensure high-quality software. Therefore, boundary value analysis and testing have been a fundamental part of the software testing toolbox for a long time and are typically taught early to software engineering students. Despite its many argued benefits, boundary value analysis for a given software specification or application is typically described in abstract terms. This allows for variation in how testers apply it and in the benefits they see. Additionally, its adoption has been limited since it requires a specification or model to be analysed. We propose an automated black-box boundary value detection method to support software testers in performing systematic boundary value analysis. This dynamic method can be utilized even without a specification or model. The proposed method is based on a metric referred to as the program derivative, which quantifies the level of boundariness of test inputs. By combining this metric with search algorithms, we can identify and rank pairs of inputs as good boundary candidates, i.e., inputs that are in close proximity to each other but with outputs that are far apart. We have implemented the AutoBVA approach and evaluated it on a curated dataset of example programs. Furthermore, we have applied the approach broadly to a sample of 613 functions from the base library of the Julia programming language. The approach could identify boundary candidates that highlight diverse boundary behaviours in over 70% of investigated systems under test. The results demonstrate that even a simple variant of the program derivative, combined with broad sampling and search over the input space, can identify interesting boundary candidates for a significant portion of the functions under investigation. In conclusion, we also discuss the future extension of the approach to encompass more complex systems under test cases and datatypes.
| S-EPMC10702978 | biostudies-literature
Project description:Recent research comparing mental models theory and causal Bayes nets for their ability to account for discounting and augmentation inferences in causal conditional reasoning had some limitations. One of the experiments used an ordinal scale and multiple items and analysed the data by subjects and items. This procedure can create a variety of problems that can be resolved by using an appropriate cumulative link function mixed models approach in which items are treated as random effects. Experiment 1 replicated this earlier experiment and analysed the results using appropriate data analytic techniques. Although successfully replicating earlier research, the pattern of results could be explained by a much simpler "shallow encoding" hypothesis. Experiment 2 introduced a manipulation to critically test this hypothesis. The results favoured the causal Bayes nets predictions and not shallow encoding and were not consistent with mental models theory. Experiment 1 provided qualified support for the causal Bayes net approach using appropriate statistics because it also replicated the failure to observe one of the predicted main effects. Experiment 2 discounted one plausible explanation for this failure. While within the limited goals that were set for these experiments they were successful, more research is required to account for the pattern of findings using this paradigm.
| S-EPMC5193512 | biostudies-other
Project description:We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., , saliency maps) that assess feature importance do not explain how imaging features in important anatomical regions are relevant to the classification decision. Such reasoning is crucial for transparent decision-making in healthcare applications. Our framework explains the decision for a target class by gradually exaggerating the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. Our proposed loss function preserves essential details (e.g., support devices) in the generated images. We used counterfactual explanations from our framework to audit a classifier trained on a chest X-ray dataset with multiple labels. Clinical evaluation of model explanations is a challenging task. We proposed clinically-relevant quantitative metrics such as cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate our explanations. We used these metrics to quantify the counterfactual changes between the populations with negative and positive decisions for a diagnosis by the given classifier. We conducted a human-grounded experiment with diagnostic radiology residents to compare different styles of explanations (no explanation, saliency map, cycleGAN explanation, and our counterfactual explanation) by evaluating different aspects of explanations: (1) understandability, (2) classifier's decision justification, (3) visual quality, (d) identity preservation, and (5) overall helpfulness of an explanation to the users. Our results show that our counterfactual explanation was the only explanation method that significantly improved the users' understanding of the classifier's decision compared to the no-explanation baseline. Our metrics established a benchmark for evaluating model explanation methods in medical images. Our explanations revealed that the classifier relied on clinically relevant radiographic features for its diagnostic decisions, thus making its decision-making process more transparent to the end-user.
| S-EPMC9835100 | biostudies-literature
Project description:BackgroundSilicon (Si) is widely considered a non-essential but beneficial element for higher plants, providing broad protection against various environmental stresses (both biotic and abiotic), particularly in species that can readily absorb the element. Two plasma-membrane proteins are known to coordinate the radial transport of Si (in the form of Si(OH)4) from soil to xylem within roots: the influx channel Lsi1 and the efflux transporter Lsi2. From a structural and mechanistic perspective, much more is known about Lsi1 (a member of the NIP-III subgroup of the Major Intrinsic Proteins) compared to Lsi2 (a putative Si(OH)4/H+ antiporter, with some homology to bacterial anion transporters).ScopeHere, we critically review the current state of understanding regarding the physiological role and molecular characteristics of Lsi2. We demonstrate that the structure-function relationship of Lsi2 is largely uncharted and that the standing transport model requires much better supportive evidence. We also provide (to our knowledge) the most current and extensive phylogenetic analysis of Lsi2 from all fully sequenced higher-plant genomes. We end by suggesting research directions and hypotheses to elucidate the properties of Lsi2.ConclusionsGiven that Lsi2 is proposed to mediate xylem Si loading and thus root-to-shoot translocation and biosilicification, it is imperative that the field of Si transport focus its efforts on a better understanding of this important topic. With this review, we aim to stimulate and advance research in the field of Si transport and thus better exploit Si to improve crop resilience and agricultural output.Supplementary informationThe online version contains supplementary material available at 10.1007/s11104-021-05061-1.
| S-EPMC8550040 | biostudies-literature
Project description:Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms. However, despite rapid progress in this area, pathway modeling can become a real nightmare for researchers, notably when few experimental data are available or when the pathway is highly complex. Here, three different approaches were developed to model the second part of glycolysis of E. histolytica as an application example, and have succeeded in predicting the final pathway flux: one including detailed kinetic information (white-box), another with an added adjustment term (grey-box) and the last one using an artificial neural network method (black-box). Afterwards, each model was used for metabolic control analysis and flux control coefficient determination. The first two enzymes of this pathway are identified as the key enzymes playing a role in flux control. This study revealed the significance of the three methods for building suitable models adjusted to the available data in the field of metabolic pathway modeling, and could be useful to biologists and modelers.
| S-EPMC7417601 | biostudies-literature
Project description:This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.
| S-EPMC4211462 | biostudies-literature
Project description:IntroductionBlack box warnings (BBWs) are the strongest medication-related safety warnings in a drug's labeling information and highlight major risks. Absence of a BBW or asynchronous addition of a BBW among same-class drugs could have major implications.MethodsWe identified the 20 top-selling drugs in 2008 (10 with BBWs and 10 without BBWs on their label) that belonged to different drug classes. We collected labeling information on all drugs belonging in these 20 classes, and recorded differences in the presence and timing of acquisition of BBWs for same-class drugs.ResultsAcross the 20 evaluated drug classes, we identified 176 different agents, of which 7 had been withdrawn for safety reasons. The reasons for the withdrawals became BBWs in other same-class agents only in two of the seven cases. Differences were identified in 9 of the 20 classes corresponding to 15 BBWs that were not present in all drugs of the same class. The information for 10 of the 15 different BBWs were included in the labels of same-class drugs as simple warnings or text, while it was absent entirely in 5 BBWs. The median interval from the time the BBW had appeared in another drug of the same class was 66 months.DiscussionDifferences in BBW labeling in same-class drugs are common and shape impressions about the safety of similar agents. BBW labeling needs to become more systematic.
| S-EPMC3101972 | biostudies-literature
Project description:RNA cleavage transesterification is of fundamental reaction in biology that is catalyzed by both protein and RNA enzymes. In this work, a series of RNA transesterification model reactions with a wide range of leaving groups are investigated with density-functional calculations in an aqueous solvation environment in order to study linear free energy relationships (LFERs) and their connection to transition state structure and bonding. Overall, results obtained from the polarizable continuum solvation model with UAKS radii produce the best linear correlations and closest overall agreement with experimental results. Reactions with a poor leaving group are predicted to proceed via a stepwise mechanism with a late transition state that is rate controlling. As leaving group becomes more acidic and labile, the barriers of both early and late transition states decrease. LFERs for each transition state are computed, with the late transition state barrier showing greater sensitivity to leaving group pKa. For sufficiently enhanced leaving groups, the reaction mechanism transits to a concerted mechanism characterized by a single early transition state. Further linear relationships were derived for bond lengths and bond orders as a function of leaving group pKa and rate constant values that can be used for prediction. This work provides important benchmark linear free energy data that allows a molecular-level characterization of the structure and bonding of the transition states for this important class of phosphoryl transfer reactions. The relations reported herein can be used to aid in the interpretation of data obtained from experimental studies of non-catalytic and catalytic mechanisms.
| S-EPMC4366550 | biostudies-literature