Causes Of Problems Research Articles

Joint Guidance: A Capacity to Jointly Guide

Abstract Sometimes, we act in concert with others, as when we go for a walk together, or when two mathematicians try to prove a difficult theorem with each other. An interesting question is what distinguishes the actions of individuals that together constitute some joint activity from those that amount to a mere aggregation of individual behaviours. It is common for philosophers to appeal to collective intentionality to explain such instances of shared agency. This framework generalizes the approach traditionally used to explain individual action: a behaviour is an action just in case it causally follows from the relevant intention. Contemporary philosophers of action, as well as cognitive psychologists, however, have criticised this way of explaining individual actions, favouring instead an approach that puts “control” or “guidance” as the discerning factor: a behaviour is an action just in case the agent controls it, or just in case it is guided by the agent. In this paper, I argue that we should include talk of guidance even in cases of joint action. I first show that problems of deviant causation arise also in cases of joint action, and that therefore guidance is required to face this issue. Then, I show what a “capacity to jointly guide” amounts to for an individual in a group and how joint guidance relates to individual guidance. Joint guidance is actually constituted by task co-representation and the sense of being jointly committed. I argue that an approach that favours joint guidance over collective intentions eschews a lot of metaphysical problems about collective mentality and group subjects, and it is thus more explanatorily fruitful.

Failures of Scale Separation in Biology and the Problem of Inter-Level Causation

ABSTRACT A conflict between evidence for causation and the metaphysical requirement of spatiotemporal distinct causal relata arises if the results of experiments commonly described as ‘bottom-up’ and ‘top-down’ are taken to demonstrate ontological determination dependencies between parts and wholes or their respective behaviours. It has been argued that the problem can be circumvented if experimental results are interpreted in terms of relationships between variables measured and manipulated at separating scales. I argue that scale separation fails in the case of biological phenomena due to the small number of molecules and molecular events underlying these phenomena, which makes it impossible to completely disentangle micro and macro-scale behaviours. I defend instead a causal mediation interpretation according to which experiments assess the effects of interventions targeting the composition and exposure of biological systems. Under this interpretation, causal relata are ontologically distinct fractions or parts of biological systems, thus avoiding the oddity of part-whole causation.

Grounding Causal Closure or Something Near Enough

AbstractA causal argument for physicalism is widely held to pose a problem for dualism. This view has an unobvious presupposition, namely that the causal closure of the physical has a special sort of ground. The requisite sort of ground must distinguish the causal argument for physicalism from many defective causal arguments. On behalf of physicalists, I develop an account of the ground for the causal closure of the physical, thereby putting the causal argument for physicalism back in the business of causally problematizing dualism. One consequence of my account is that physicalists can pose a causal problem for dualism using a much weaker closure premise than is generally assumed.

The epistemic problem of singular causation

The epistemic problem of singular causation

Robust Bayesian causal estimation for causal inference in medical diagnosis

Causal effect estimation is a critical task in statistical learning that aims to find the causal effect on subjects by identifying causal links between a number of predictor (or, explanatory) variables and the outcome of a treatment. In a regressional framework, we assign a treatment and outcome model to estimate the average causal effect. Additionally, for high dimensional regression problems, variable selection methods are also used to find a subset of predictor variables that maximises the predictive performance of the underlying model for better estimation of the causal effect. In this paper, we propose a different approach. We focus on the variable selection aspects of high dimensional causal estimation problem. We suggest a cautious Bayesian group LASSO (least absolute shrinkage and selection operator) framework for variable selection using prior sensitivity analysis. We argue that in some cases, abstaining from selecting (or, rejecting) a predictor is beneficial and we should gather more information to obtain a more decisive result. We also show that for problems with very limited information, expert elicited variable selection can give us a more stable causal effect estimation as it avoids overfitting. Lastly, we carry a comparative study with synthetic dataset and show the applicability of our method in real-life situations.

Estimating Time-Varying Exposure Effects Through Continuous-Time Modelling in Mendelian Randomization.

Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time-varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time-varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time-varying causal problems. This article considers a novel approach to estimate time-varying effects through continuous-time modelling by combining functional principal component analysis and weak-instrument-robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time-varying effects and provides reliable inference when the time-varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time-varying effects. However, there is a trade-off between model complexity and instrument strength. Estimating complex time-varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time-varying effects of systolic blood pressure on urea levels.

Identifiability and Estimation for Potential-Outcome Means with Misclassified Outcomes

Potential outcomes play a fundamental and important role in many causal inference problems. If the potential-outcome means are identifiable, a series of causal effect measures, including the risk difference, the risk ratio, and the treatment benefit rate, among others, can also be identified. However, current identification and estimation methods for these means often implicitly assume that the collected data for analysis are measured precisely. In many fields such as medicine and economics, the collected variables may be subject to measurement errors, such as medical diagnostic results and individual wage data. Misclassification, as a non-classic measurement error, can lead to severely biased estimates in causal inference. In this paper, we leverage a combined sample to study the identifiability of potential-outcome means corresponding to different treatment levers under a plausible misclassification assumption for the outcome, allowing the misclassification probability to depend on not only the true outcome but also the covariates. Furthermore, we propose the multiply-robust and semiparametric efficient estimators for the means, consistent even under partial misspecification of the observed data law, based on the semiparametric theory framework. The simulation studies and real data analysis demonstrate the satisfactory performance of the proposed method.

A non-causalist account of the explanatory autonomy in the psychological sciences

It has been often claimed that physicalism challenges the explanatory autonomy of psychological sciences. Most who advocate for such explanatory autonomy and do not want to renounce to physicalism, presuppose a causalist account of explanatoriness and try to demonstrate that, adequately construed, (causal) psychological explanations are compatible with (some sufficient version of) physicalism. In Sect. 1 we summarize the different theses and assumptions involved in the seeming conflict between explanatory autonomy and physicalism. In Sect. 2 we review the main attempts to make them compatible assuming a causalist account of explanation and argue that none succeeds. In Sect. 3 we introduce a recent, non-causalist account of scientific explanation as ampliative, specialized embedding (ASE) that has been successfully applied to other fields. In Sect. 4 we apply ASE to elucidate two paradigmatic cognitive explanations of psychological phenomena: déjà vu and action production. We conclude that ASE elucidates well the autonomy of the cognitive explanations of these phenomena independently of what finally happens with the causal exclusion problem and that it may be generalized to other psychological explanations.

Bayesian jackknife empirical likelihood‐based inference for missing data and causal inference

AbstractMissing data reduce the representativeness of the sample and can lead to inference problems. In this article, we apply the Bayesian jackknife empirical likelihood (BJEL) method for inference on data that are missing at random, as well as for causal inference. The semiparametric fractional imputation estimator, propensity score‐weighted estimator, and doubly robust estimator are used for constructing the jackknife pseudo values, which are needed for conducting BJEL‐based inference with missing data. Existing methods, such as normal approximation and JEL, are compared with the BJEL approach in a simulation study. The proposed approach shows better performance in many scenarios in terms of credible intervals. Furthermore, we demonstrate the application of the proposed approach for causal inference problems in a study of risk factors for impaired kidney function.

Machine learning for causal inference: An application to ECLS-K data

This paper explores the use of machine learning for causal inference to estimate the average treatment effect of special education services on fifth-grade math scores. Causal inference is the study of the relationship between cause and effect when changes in one variable directly affect another variable. The use of machine learning techniques in causal inference problems has been growing rapidly, offering advantages over traditional methods such as propensity score matching. such as propensity score matching. This paper compares the performance of four machine learning methods: Ordinary Least Squares (OLS), Multi-Layer Perception (MLP), Targeted Maximum Likelihood Estimation (TMLE), and Bayesian Additive Regression Trees (BART) in estimating the average treatment effect of special education services on fifth-grade math scores. This study utilizes the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K) dataset. A factor analysis is conducted to identify the key variables that influence math performance, paving the way for examining their causal effects. Our results show that BART outperforms the other methods in accuracy and robustness and that receiving special education services does not have a causal effect on math scores. This paper discusses the implications and limitations of our findings and suggests directions for future study.

Adaptive Neural Event-Triggered Output-Feedback Opti-mal Tracking Control for Discrete-Time Pure-Feedback Nonlinear Systems

Article Adaptive Neural Event-Triggered Output-Feedback Optimal Tracking Control for Discrete-Time Pure-Feedback Nonlinear Systems Wei Wang 1, and Min Wang 1,2,* 1 School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China 2 Pengcheng Laboratory, Shenzhen 518055, China * Correspondence: auwangmin@scut.edu.cn Received: 20 December 2023 Accepted: 30 January 2024 Published: 26 June 2024 Abstract: In this article, a novel event-triggered (ET) output-feedback optimal tracking control scheme is developed for a class of uncertain discrete-time nonlinear systems in the pure-feedback form with immeasurable states. Firstly, different from the traditional n-step-ahead input-output prediction model, the immeasurable states of the system are estimated in real time by designing a neural network (NN) state observer. Then, the implicit function theorem and the mean value theorem are combined to tackle the nonaffine terms. The variable substitution approach is applied to overcome the causal contradiction problem during the backstepping design, and meanwhile the n-step time delays caused by the traditional n-step-ahead prediction model are avoided. Subsequently, the critic NN and the action NN are employed to minimize the system long-term performance measure. Under the adaptive critic design framework, an optimal controller is designed to obtain the optimal control performance. Furthermore, an ET mechanism is embedded between sensors and controllers to reduce network burden. A novel ET condition is developed to save network resources and guarantee the desired tracking control performance. According to the Lyapunov stability analysis, all the closed-loop system signals are guaranteed to be uniformly ultimately bounded.

Causality-aware social recommender system with network homophily informed multi-treatment confounders

Typical recommender systems utilize observed ratings of users as inputs to learn their preferences and aim to output recommendations of new items that users will like by predicting their potential ratings. The real world is driven by causality given that whether or not a user is made exposed to the item may significantly affect the rating. While some recent methods have taken a causal view to mitigate the confounding bias in observed rating data, few have recognized recommendation as a multiple causal inference problem that concerns numerous items simultaneously in practice. By framing the recommendation as a multiple causal inference problem, this paper develops a causality-aware social recommender that incorporates underlying social network structures with matrix factorization methods for deconfounding in networked observational data to enhance social recommendations. Leveraging social network information, which inherently confounds with the preferences of users, is particularly crucial but not trivial for inferring unobserved confounders in social recommender systems. Considering that connected users in a social network share similar attributes, we propose to incorporate the network homophily into the matrix factorization models through regularization to better learn the latent variables. The extracted latent variables then capture the network informed multi-treatment confounders to mitigate the confounding bias and improve the rating prediction accuracy. The models are estimated through a proximal gradient-based optimization framework, which not only eases the incorporation of the network structure constraints but also improves the computational efficiency of the proposed method. Simulation and case studies are conducted to validate the proposed method.

Identification of causal influences in quantum processes

Recent years have seen great interest in extending causal inference concepts developed in the context of classical statistical models to quantum theory. So far, this program has only barely addressed causal identification, a type of causal inference problem concerned with recovering from observational data and qualitative assumptions the causal relationships generating the data, and hence the effects of hypothetical interventions. A major obstacle to a theory of causal identification in the quantum setting is the question of what should play the role of “observational data,” as any means of extracting data at a locus will almost certainly disturb the system. One might think that quantum measurements are already too much like interventions, so that the problem of causal identification is trivialized. This is not the case: when we fix a limited class of quantum measurement instruments (namely, the class of all projective measurements) to play the role of “observations,” there exist scenarios for which causal identification is impossible. In this paper, we present a framework, based on process theories (also known as strict symmetric monoidal categories), for studying quantum causal identification scenarios on the same footing as their classical counterparts. Within this framework, we present sufficient conditions for quantum causal identification in networks with unobserved confounding systems, including quantum analogs of the well-known “back-door” and “front-door” criteria. These results arise from a type of causal model designed to facilitate the transfer of inference techniques from the classical to the quantum setting. Published by the American Physical Society 2024

De novo drug design as GPT language modeling: large chemistry models with supervised and reinforcement learning

In recent years, generative machine learning algorithms have been successful in designing innovative drug-like molecules. SMILES is a sequence-like language used in most effective drug design models. Due to data’s sequential structure, models such as recurrent neural networks and transformers can design pharmacological compounds with optimized efficacy. Large language models have advanced recently, but their implications on drug design have not yet been explored. Although one study successfully pre-trained a large chemistry model (LCM), its application to specific tasks in drug discovery is unknown. In this study, the drug design task is modeled as a causal language modeling problem. Thus, the procedure of reward modeling, supervised fine-tuning, and proximal policy optimization was used to transfer the LCM to drug design, similar to Open AI’s ChatGPT and InstructGPT procedures. By combining the SMILES sequence with chemical descriptors, the novel efficacy evaluation model exceeded its performance compared to previous studies. After proximal policy optimization, the drug design model generated molecules with 99.2% having efficacy pIC50 > 7 towards the amyloid precursor protein, with 100% of the generated molecules being valid and novel. This demonstrated the applicability of LCMs in drug discovery, with benefits including less data consumption while fine-tuning. The applicability of LCMs to drug discovery opens the door for larger studies involving reinforcement-learning with human feedback, where chemists provide feedback to LCMs and generate higher-quality molecules. LCMs’ ability to design similar molecules from datasets paves the way for more accessible, non-patented alternatives to drug molecules.

Probabilistic Causal Effect Estimation With Global Neural Network Forecasting Models.

We introduce a novel method to estimate the causal effects of an intervention over multiple treated units by combining the techniques of probabilistic forecasting with global forecasting methods using deep learning (DL) models. Considering the counterfactual and synthetic approach for policy evaluation, we recast the causal effect estimation problem as a counterfactual prediction outcome of the treated units in the absence of the treatment. Nevertheless, in contrast to estimating only the counterfactual time series outcome, our work differs from conventional methods by proposing to estimate the counterfactual time series probability distribution based on the past preintervention set of treated and untreated time series. We rely on time series properties and forecasting methods, with shared parameters, applied to stacked univariate time series for causal identification. This article presents DeepProbCP, a framework for producing accurate quantile probabilistic forecasts for the counterfactual outcome, based on training a global autoregressive recurrent neural network model with conditional quantile functions on a large set of related time series. The output of the proposed method is the counterfactual outcome as the spline-based representation of the counterfactual distribution. We demonstrate how this probabilistic methodology added to the global DL technique to forecast the counterfactual trend and distribution outcomes overcomes many challenges faced by the baseline approaches to the policy evaluation problem. Oftentimes, some target interventions affect only the tails or the variance of the treated units' distribution rather than the mean or median, which is usual for skewed or heavy-tailed distributions. Under this scenario, the classical causal effect models based on counterfactual predictions are not capable of accurately capturing or even seeing policy effects. By means of empirical evaluations of synthetic and real-world datasets, we show that our framework delivers more accurate forecasts than the state-of-the-art models, depicting, in which quantiles, the intervention most affected the treated units, unlike the conventional counterfactual inference methods based on nonprobabilistic approaches.

Irruption and Absorption: A 'Black-Box' Framework for How Mind and Matter Make a Difference to Each Other.

Cognitive science is confronted by several fundamental anomalies deriving from the mind-body problem. Most prominent is the problem of mental causation and the hard problem of consciousness, which can be generalized into the hard problem of agential efficacy and the hard problem of mental content. Here, it is proposed to accept these explanatory gaps at face value and to take them as positive indications of a complex relation: mind and matter are one, but they are not the same. They are related in an efficacious yet non-reducible, non-observable, and even non-intelligible manner. Natural science is well equipped to handle the effects of non-observables, and so the mind is treated as equivalent to a hidden 'black box' coupled to the body. Two concepts are introduced given that there are two directions of coupling influence: (1) irruption denotes the unobservable mind hiddenly making a difference to observable matter, and (2) absorption denotes observable matter hiddenly making a difference to the unobservable mind. The concepts of irruption and absorption are methodologically compatible with existing information-theoretic approaches to neuroscience, such as measuring cognitive activity and subjective qualia in terms of entropy and compression, respectively. By offering novel responses to otherwise intractable theoretical problems from first principles, and by doing so in a way that is closely connected with empirical advances, irruption theory is poised to set the agenda for the future of the mind sciences.

Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise

This paper considers learning the hidden causal network of a linear networked dynamical system (NDS) from the time series data at some of its nodes -- partial observability. The dynamics of the NDS are driven by colored noise that generates spurious associations across pairs of nodes, rendering the problem much harder. To address the challenge of noise correlation and partial observability, we assign to each pair of nodes a feature vector computed from the time series data of observed nodes. The feature embedding is engineered to yield structural consistency: there exists an affine hyperplane that consistently partitions the set of features, separating the feature vectors corresponding to connected pairs of nodes from those corresponding to disconnected pairs. The causal inference problem is thus addressed via clustering the designed features. We demonstrate with simple baseline supervised methods the competitive performance of the proposed causal inference mechanism under broad connectivity regimes and noise correlation levels, including a real world network. Further, we devise novel technical guarantees of structural consistency for linear NDS under the considered regime.

S-ID: Causal Effect Identification in a Sub-population

Causal inference in a sub-population involves identifying the causal effect of an intervention on a specific subgroup, which is distinguished from the whole population through the influence of systematic biases in the sampling process. However, ignoring the subtleties introduced by sub-populations can either lead to erroneous inference or limit the applicability of existing methods. We introduce and advocate for a causal inference problem in sub-populations (henceforth called s-ID), in which we merely have access to observational data of the targeted sub-population (as opposed to the entire population). Existing inference problems in sub-populations operate on the premise that the given data distributions originate from the entire population, thus, cannot tackle the s-ID problem. To address this gap, we provide necessary and sufficient conditions that must hold in the causal graph for a causal effect in a sub-population to be identifiable from the observational distribution of that sub-population. Given these conditions, we present a sound and complete algorithm for the s-ID problem.

Problems of Criminological Determination and Causation in the Scientific Doctrine of Professor Vitaliy Anatolyevich Nomokonov

A famous Russian criminologist, Doctor of Law, Professor, Corresponding Member of the Russian Academy of Natural Sciences, Vice-President of the Russian Criminological Association, author of over 400 research works and textbooks Vitaliy Anatolyevich Nomokonov has celebrated his 75th birthday. He is a well-known figure in both Russian and foreign criminology famous for his works, his conceptual and fundamental scientific views and approaches, unfailing principles and loyalty to universal human values when developing the strategy and tactics of corruption counteraction. Being one of the founders of a highly complex problem area in the field of criminology — the theory of crime determination and causation — he also consistently studies the modern aspects of this theory. The Professor was also one of the first scientists who researched the mechanisms of counteracting organized crime and corruption. He has been studying the regional aspects of these interconnected forms of crime over a long period of time, as well as promoting the ideas of its prevention at the political and public levels. Advocating the proactive development of criminology and its methodological basis along the lines of the transforming society, the Professor has been developing new directions in criminology stressing the anticriminal and anticorruption policy. Positive criminology is a trend in his works, he argues for the humane-personal approach in the theory of the criminal’s personality and proves the importance of understanding the regularities of positive law-abiding behavior and identifying the anti-criminogenic factors underlying it. The number of his students and followers is constantly growing. Over 15 researcher, who wrote their dissertation thesis under V.A. Nomokonov’s supervision, have become the founders of the theory of cybercrime, they promote his ideas regarding anticorruption policy, drug abuse, human trafficking, etc., and have the law enforcement experience that they successfully use in their current work. The Professor’s approaches to understanding organized crime and corruption, as well as the basic and special measures of counteracting these dangerous phenomena, are a safe basis for the corresponding criminological theory, they reflect his forward-thinking and astute attitude. His scientific intuition allows the Professor to be ahead of other research in the sphere of counteracting corruption and provide highly professional predictions on the future regularities of criminal behavior, thus contributing to the development and enriching the theory of criminology and its practical significance.

Fodor’un Asimetrik Bağımlılık Teorisi

Fodor’s theory of intentionality can be interpreted as consisting of two parts: (1) theory about propositional attitudes and (2) theory of content or meaning. In this paper we will focus mainly on his theory of content, particularly his theory of asymmetric dependence on the problem of disjunction, since it is at the heart of Fodor’s theory of representation. Fodor’s theory of content is a well-known attempt to naturalize mental representation and one of the most important parts of his theory is the notion of asymmetric dependence. He offers it as a solution to the problem of disjunction. In this context, we will examine his theory of content, and particularly his notion of asymmetric dependence. After summarizing his theory, we will discuss that asymmetric dependence may have some weaknesses that require some revision. Fodor modified his theory considering objections to his earlier work. However, there may still be some problems that he needs to solve. Accordingly, we will identify three important challenges to the theory of asymmetric dependence, namely the problem of unjustified properties, the problem of pathologies, and the problem of wild causation. Finally, we will give some answers to these challenges on behalf of Fodor and discuss that Fodor’s theory may overcome all these problems.