Relevant bibliographies by topics / Causal machine learning

Academic literature on the topic 'Causal machine learning'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Causal machine learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Causal machine learning"

1

Weiser, Michael, Stefan Feuerriegel, and Tim Herrmann. "Causal Machine Learning." Controlling 32, no. 3 (2020): 86–87. http://dx.doi.org/10.15358/0935-0381-2020-3-86.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Walker, Caren M., Alexandra Rett, and Elizabeth Bonawitz. "Design Drives Discovery in Causal Learning." Psychological Science 31, no. 2 (January 21, 2020): 129–38. http://dx.doi.org/10.1177/0956797619898134.

Full text
Abstract:
We assessed whether an artifact’s design can facilitate recognition of abstract causal rules. In Experiment 1, 152 three-year-olds were presented with evidence consistent with a relational rule (i.e., pairs of same or different blocks activated a machine) using two differently designed machines. In the standard-design condition, blocks were placed on top of the machine; in the relational-design condition, blocks were placed into openings on either side. In Experiment 2, we assessed whether this design cue could facilitate adults’ ( N = 102) inference of a distinct conjunctive cause (i.e., that two blocks together activate the machine). Results of both experiments demonstrated that causal inference is sensitive to an artifact’s design: Participants in the relational-design conditions were more likely to infer rules that were a priori unlikely. Our findings suggest that reasoning failures may result from difficulty generating the relevant rules as cognitive hypotheses but that artifact design aids causal inference. These findings have clear implications for creating intuitive learning environments.
APA, Harvard, Vancouver, ISO, and other styles
3

Zhao, Yang, and Qing Liu. "Causal ML: Python package for causal inference machine learning." SoftwareX 21 (February 2023): 101294. http://dx.doi.org/10.1016/j.softx.2022.101294.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Goodman, Steven N., Sharad Goel, and Mark R. Cullen. "Machine Learning, Health Disparities, and Causal Reasoning." Annals of Internal Medicine 169, no. 12 (December 4, 2018): 883. http://dx.doi.org/10.7326/m18-3297.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Huenermund, Paul, Jermain Christopher Kaminski, and Carla Schmitt. "Causal Machine Learning and Business Decision Making." Academy of Management Proceedings 2021, no. 1 (August 2021): 12517. http://dx.doi.org/10.5465/ambpp.2021.12517abstract.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Jung, Yonghan, Jin Tian, and Elias Bareinboim. "Estimating Identifiable Causal Effects through Double Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 12113–22. http://dx.doi.org/10.1609/aaai.v35i13.17438.

Full text
Abstract:
Identifying causal effects from observational data is a pervasive challenge found throughout the empirical sciences. Very general methods have been developed to decide the identifiability of a causal quantity from a combination of observational data and causal knowledge about the underlying system. In practice, however, there are still challenges to estimating identifiable causal functionals from finite samples. Recently, a method known as double/debiased machine learning (DML) (Chernozhukov et al. 2018) has been proposed to learn parameters leveraging modern machine learning techniques, which is both robust to model misspecification and bias-reducing. Still, DML has only been used for causal estimation in settings when the back-door condition (also known as conditional ignorability) holds. In this paper, we develop a new, general class of estimators for any identifiable causal functionals that exhibit DML properties, which we name DML-ID. In particular, we introduce a complete identification algorithm that returns an influence function (IF) for any identifiable causal functional. We then construct the DML estimator based on the derived IF. We show that DML-ID estimators hold the key properties of debiasedness and doubly robustness. Simulation results corroborate with the theory.
APA, Harvard, Vancouver, ISO, and other styles
7

Arti, Shindy, Indriana Hidayah, and Sri Suning Kusumawardhani. "Research Trend of Causal Machine Learning Method: A Literature Review." IJID (International Journal on Informatics for Development) 9, no. 2 (December 31, 2020): 111–18. http://dx.doi.org/10.14421/ijid.2020.09208.

Full text
Abstract:
Machine learning is commonly used to predict and implement pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.
APA, Harvard, Vancouver, ISO, and other styles
8

Sasou, Akira. "Deep Residual Learning With Dilated Causal Convolution Extreme Learning Machine." IEEE Access 9 (2021): 165708–18. http://dx.doi.org/10.1109/access.2021.3134700.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Zhao, Yiqing, Yue Yu, Hanyin Wang, Yikuan Li, Yu Deng, Guoqian Jiang, and Yuan Luo. "Machine Learning in Causal Inference: Application in Pharmacovigilance." Drug Safety 45, no. 5 (May 2022): 459–76. http://dx.doi.org/10.1007/s40264-022-01155-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Crown, William H. "Real-World Evidence, Causal Inference, and Machine Learning." Value in Health 22, no. 5 (May 2019): 587–92. http://dx.doi.org/10.1016/j.jval.2019.03.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Causal machine learning"

1

Moffett, Jeffrey P. "Applying Causal Models to Dynamic Difficulty Adjustment in Video Games." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/320.

Full text
Abstract:
We have developed a causal model of how various aspects of a computer game influence how much a player enjoys the experience, as well as how long the player will play. This model is organized into three layers: a generic layer that applies to any game, a refinement layer for a particular game genre, and an instantiation layer for a specific game. Two experiments using different games were performed to validate the model. The model was used to design and implement a system and API for Dynamic Difficulty Adjustment(DDA). This DDA system and API uses machine learning techniques to make changes to a game in real time in the hopes of improving the experience of the user and making them play longer. A final experiment is presented that shows the effectiveness of the designed system.
APA, Harvard, Vancouver, ISO, and other styles
2

Bethard, Steven John. "Finding event, temporal and causal structure in text: A machine learning approach." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3284435.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Balsa, Fernández Juan José. "Using causal tree algorithms with difference in difference methodology : a way to have causal inference in machine learning." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/168527.

Full text
Abstract:
TESIS PARA OPTAR AL GRADO DE MAGISTER EN ANÁLISIS ECONÓMICO
been for a long time one of the main focus of the economist around the world. At the same time, the development of different statistical methodologies have deeply helps them to complement the economic theory with the different types of data. One of the newest developments in this area is the Machine Learning algorithms for Causal inference, which gives them the possibility of using huge amounts of data, combined with computational tools for much more precise results. Nevertheless, these algorithms have not implemented one of the most used methodologies in the public evaluation, the Difference in Difference methodology. This document proposes an estimator that combines the Honest Causal Tree of Athey and Imbens (2016) with the Difference in Difference framework, giving us the opportunity to obtain heterogeneous treatment effect. Although the proposed estimator has higher levels of Bias, MSE, and Variance in comparison with the OLS, it is able to find significant results in cases where OLS do not, and instead of estimate an Average Treatment Effect, it is able to estimate a treatment effect for each individual.
APA, Harvard, Vancouver, ISO, and other styles
4

Goh, Siong Thye. "Machine learning approaches to challenging problems : interpretable imbalanced classification, interpretable density estimation, and causal inference." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119281.

Full text
Abstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 111-118).
In this thesis, I address three challenging machine-learning problems. The first problem that we address is the imbalanced data problem. We propose two algorithms to handle highly imbalanced classification problems. The first algorithm uses mixed integer programming to optimize a weighted balance between positive and negative class accuracies. The second method uses an approximation in order to assist with scalability. Specifically, it follows a characterize-then-discriminate approach. The positive class is first characterized by boxes, and then each box boundary becomes a separate discriminative classifier. This method is computationally advantageous because it can be easily parallelized, and considers only the relevant regions of the feature space. The second problem is a density estimation problem for categorical data sets. We present tree- and list- structured density estimation methods for binary/categorical data. We present three generative models, where the first one allows the user to specify the number of desired leaves in the tree within a Bayesian prior. The second model allows the user to specify the desired number of branches within the prior. The third model returns lists (rather than trees) and allows the user to specify the desired number of rules and the length of rules within the prior. Finally, we present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes. Strictly, both treatment and control outcomes must be measured for each unit in order to perform supervised learning. However, in practice, only one outcome can be observed per unit. To overcome the problem that both treatment and control outcomes for the same unit are required for supervised learning, we propose surrogate loss functions that incorporate both treatment and control data. The new surrogates yield tighter bounds than the sum of the losses for the treatment and control groups. A specific choice of loss function, namely a type of hinge loss, yields a minimax support vector machine formulation. The resulting optimization problem requires the solution to only a single convex optimization problem, incorporating both treatment and control units, and it enables the kernel trick to be used to handle nonlinear (also non-parametric) estimation.
by Siong Thye Goh.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
5

Hagerty, Nicholas L. "Bayesian Network Modeling of Causal Relationships in Polymer Models." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1619009432971036.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lash, Michael Timothy. "Optimizing outcomes via inverse classification." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6602.

Full text
Abstract:
In many circumstances, predictions elicited from induced classification models are useful to a certain extent, as such predictions provide insight into what the future may hold. Such models, in and of themselves, hold little value beyond making such predictions, as they are unable to inform their user as to how to change a predicted outcome. Consider, for example, a health care domain where a classification model has been induced to learn the mapping from patient characteristics to disease outcome. A patient may want to know how to lessen their probability of developing such a disease. In this document, four different approaches to inverse classification, the process of turning predictions into prescriptions by working backwards through an induced classification model to optimize for a particular outcome of interest, are explored. The first study develops an inverse classification framework, which is created to produce instance-specific, real-world feasible recommendations that optimally improve the probability of a good outcome, while being as classifier-permissive as possible. Real-world feasible recommendations are obtained by imposition of constraints that specify which features can be optimized over and accounts for user-specific preferences. Assumptions are made as to the differentiability of the classification function, permitting the use of classifiers with exploitable gradient information, such as support vector machines (SVMs) and logistic regression. Our results show that the framework produces real-world recommendations that successfully reduce the probability of a negative outcome. In the second study, we further relax our assumptions as to the differentiability of the classifier, allowing virtually any classification function to be used. Correspondingly, we adjust our optimization methodology. To such an end, three heuristic-based optimization methods are devised. Furthermore, non-linear (quadratic) relationships between feature changes and so-called cost, which accounts for user preferences, are explored. The results suggest that non-differentiable classifiers, such as random forests, can be successfully navigated using the specified framework and updated, heuristic-based optimization methodology. Furthermore, findings suggest that regularizers, encouraging sparse solutions, should be used when quadratic/non-linear cost-change relationships are specified. The third study takes a longitudinal approach to the problem, exploring the effects of applying the inverse classification process to instances across time. Furthermore, we explore the use of added temporal linkages, in the form of features representing past predicted outcome probability (i.e., risk), on the inverse classification results. We further explore and propose a solution to a missing data subproblem that frequently arises in longitudinal data settings. In the fourth and final study, a causal formulation of the inverse classification framework is provided and explored. The formulation encompasses a Gaussian Process-based method of inducing causal classifiers, which is subsequently leveraged when the inverse classification process is applied. Furthermore, exploration of the addition of certain dependencies is explored. The results suggest the importance of including such dependencies and the benefits of taking a causal approach to the problem.
APA, Harvard, Vancouver, ISO, and other styles
7

Kaiser, Michael Rainer Johann [Verfasser], and Florian [Akademischer Betreuer] Englmaier. "From causal inference to machine learning : four essays in empirical economics / Michael Rainer Johann Kaiser ; Betreuer: Florian Englmaier." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2021. http://d-nb.info/1229835709/34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Miranda, Ackerman Eduardo Jacobo. "Extracting Causal Relations between News Topics from Distributed Sources." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-130066.

Full text
Abstract:
The overwhelming amount of online news presents a challenge called news information overload. To mitigate this challenge we propose a system to generate a causal network of news topics. To extract this information from distributed news sources, a system called Forest was developed. Forest retrieves documents that potentially contain causal information regarding a news topic. The documents are processed at a sentence level to extract causal relations and news topic references, these are the phases used to refer to a news topic. Forest uses a machine learning approach to classify causal sentences, and then renders the potential cause and effect of the sentences. The potential cause and effect are then classified as news topic references, these are the phrases used to refer to a news topics, such as “The World Cup” or “The Financial Meltdown”. Both classifiers use an algorithm developed within our working group, the algorithm performs better than several well known classification algorithms for the aforementioned tasks. In our evaluations we found that participants consider causal information useful to understand the news, and that while we can not extract causal information for all news topics, it is highly likely that we can extract causal relation for the most popular news topics. To evaluate the accuracy of the extractions made by Forest, we completed a user survey. We found that by providing the top ranked results, we obtained a high accuracy in extracting causal relations between news topics.
APA, Harvard, Vancouver, ISO, and other styles
9

Hazan, Amaury. "Musical expectation modelling from audio : a causal mid-level approach to predictive representation and learning of spectro-temporal events." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/22721.

Full text
Abstract:
We develop in this thesis a computational model of music expectation, which may be one of the most important aspects in music listening. Many phenomenons related to music listening such as preference, surprise or emo- tions are linked to the anticipatory behaviour of listeners. In this thesis, we concentrate on a statistical account to music expectation, by modelling the processes of learning and predicting spectro-temporal regularities in a causal fashion. The principle of statistical modelling of expectation can be applied to several music representations, from symbolic notation to audio signals. We first show that computational learning architectures can be used and evaluated to account behavioral data concerning auditory perception and learning. We then propose a what/when representation of musical events which enables to sequentially describe and learn the structure of acoustic units in musical audio signals. The proposed representation is applied to describe and anticipate timbre features and musical rhythms. We suggest ways to exploit the properties of the expectation model in music analysis tasks such as structural segmentation. We finally explore the implications of our model for interactive music applications in the context of real-time transcription, concatenative synthesis, and visualization.
Esta tesis presenta un modelo computacional de expectativa musical, que es un aspecto muy importante de como procesamos la música que oímos. Muchos fenómenos relacionados con el procesamiento de la música están vinculados a una capacidad para anticipar la continuación de una pieza de música. Nos enfocaremos en un acercamiento estadístico de la expectativa musical, modelando los procesos de aprendizaje y de predicción de las regularidades espectro-temporales de forma causal. El principio de modelado estadístico de la expectativa se puede aplicar a varias representaciones de estructuras musicales, desde las notaciones simbólicas a la señales de audio. Primero demostramos que ciertos algoritmos de aprendizaje de secuencias se pueden usar y evaluar en el contexto de la percepción y el aprendizaje de secuencias auditivas. Luego, proponemos una representación, denominada qué/cuándo, para representar eventos musicales de una forma que permite describir y aprender la estructura secuencial de unidades acústicas en señales de audio musical. Aplicamos esta representación para describir y anticipar características tímbricas y ritmos. Sugerimos que se pueden explotar las propiedades del modelo de expectativa para resolver tareas de análisis como la segmentación estructural de piezas musicales. Finalmente, exploramos las implicaciones de nuestro modelo a la hora de definir nuevas aplicaciones en el contexto de la transcripción en tiempo real, la síntesis concatenativa y la visualización.
APA, Harvard, Vancouver, ISO, and other styles
10

Ziebart, Brian D. "Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy." Research Showcase @ CMU, 2010. http://repository.cmu.edu/dissertations/17.

Full text
Abstract:
Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Gr¨unwald & Dawid, 2003). We derive probabilistic models for decision, control, and multi-player game settings using this approach. We then develop corresponding algorithms for efficient inference that include relaxations of the Bellman equation (Bellman, 1957), and simple learning algorithms based on convex optimization. We apply the models and algorithms to a number of behavior prediction tasks. Specifically, we present empirical evaluations of the approach in the domains of vehicle route preference modeling using over 100,000 miles of collected taxi driving data, pedestrian motion modeling from weeks of indoor movement data, and robust prediction of game play in stochastic multi-player games.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Causal machine learning"

1

Borchardt, Gary C. Thinking between the lines: Computers and the comprehension of causal descriptions. Cambridge, Mass: MIT Press, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Waldmann, Michael R. Causal Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.1.

Full text
Abstract:
Although causal reasoning is a component of most human cognitive functions, it has been neglected in cognitive psychology for many decades. To date, textbooks on cognitive psychology do not contain chapters on causal reasoning. The goal of this Handbook is to fill this gap, and to offer state-of-the-art reviews of the field. This introduction to the Handbook provides a general review of different competing theoretical frameworks modeling causal reasoning and learning. It outlines the relationship between psychological theories and their precursors in normative disciplines, such as philosophy and machine learning. It reviews the wide scope of tasks and domains in which the important role of causal knowledge has been documented. In the final section it previews the chapters of the handbook.
APA, Harvard, Vancouver, ISO, and other styles
3

Elements of Causal Inference. The MIT Press, 2017.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Sekhon, Jasjeet. The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0011.

Full text
Abstract:
This article presents a detailed discussion of the Neyman-Rubin model of causal inference. Additionally, it describes under what conditions ‘matching’ approaches can lead to valid inferences, and what kinds of compromises sometimes have to be made with respect to generalizability to ensure valid causal inferences. Moreover, the article summarizes Mill's first three canons and shows the importance of taking chance into account and comparing conditional probabilities when chance variations cannot be ignored. The significance of searching for causal mechanisms is often overestimated by political scientists and this sometimes leads to an underestimate of the importance of comparing conditional probabilities. The search for causal mechanisms is probably especially useful when working with observational data. Machine learning algorithms can be used against the matching problem.
APA, Harvard, Vancouver, ISO, and other styles
5

Bertocci, Michele A., and Mary L. Phillips. Neuroimaging of Depression. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0025.

Full text
Abstract:
This chapter illustrates the historical progression, methodological approaches, and current neurobiological understanding of depression, the first leading cause of mental and behavioral disorder disability in the United States. We describe and position, in relation to depressive symptoms, the complex abnormalities that depressed adults and youth show concerning neural function during tasks and at rest, structural abnormalities, as well as key neurotransmitter, neuroreceptor, and metabolic abnormalities that have been examined in the literature. We also describe newer findings and methods such as differentiating between unipolar and bipolar depression and applying machine learning to individual prediction. Finally, we provide suggestions for future study.
APA, Harvard, Vancouver, ISO, and other styles
6

Alonso Cifuentes, Julio César, and Lina Marcela Quintero V. Guía de buenas prácticas para la mitigación del riesgo de modelo de analítica. Universidad Icesi, 2021. http://dx.doi.org/10.18046/eui/bda.g.1.

Full text
Abstract:
Las decisiones estratégicas de negocio han sido tomadas históricamente por los encargados de velar por los intereses de las empresas. Con la posibilidad de acceder a grandes volúmenes de datos, y con el desarrollo de nuevas técnicas de estadística y aprendizaje automático (Machine Learning), esta responsabilidad ha venido siendo delegada progresivamente a modelos diseñados para tal labor, con el fin de evitar el riesgo humano de equivocarse a causa de los sesgos, prejuicios y opiniones subjetivas de los tomadores de decisiones tradicionales, fundamentándose ahora en hechos objetivos inherentes a los datos operacionales de cada empresa, pero incurriendo entonces en un nuevo riesgo: que el modelo matemático delegado no logre elegir la mejor alternativa posible, o ni siquiera una adecuada [Javier Gustavo Díaz Cely].
APA, Harvard, Vancouver, ISO, and other styles
7

Stoddard Jr, Frederick J., David M. Benedek, Mohammed R. Milad, and Robert J. Ursano. Posttraumatic Stress Disorder. Edited by Frederick J. Stoddard, David M. Benedek, Mohammed R. Milad, and Robert J. Ursano. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190457136.003.0003.

Full text
Abstract:
Posttraumatic stress disorder (PTSD) affects people of all ages and backgrounds and causes persistent suffering and impaired function, but its diagnosis offers the opportunity for early intervention. It is the subject of intensive developmental, epidemiological, genetic/genomic, translational, neurobiological, neuropsychological, and psychological research, and emerging computational methods with “big data,” statistical modeling, and machine learning are likely to accelerate this research. The findings from research on PTSD are changing education and the ways clinicians practice, offering the hope for improved care of those experiencing traumatic stress. Those at particular risk for PTSD include children and adolescents, women, soldiers, refugees and survivors of genocide, sexual orientation minorities, racial and ethnic minorities, patients with burns, injuries and medical trauma, and victims of rape, violence, accidents, and disasters. This chapter provides an overview of PTSD, covering Diagnostic and Statistical Manual of Mental Disorders (fifth edition) diagnostic criteria, epidemiology, neurochemistry and neurobiology, biological and psychological models, assessment, and treatment.
APA, Harvard, Vancouver, ISO, and other styles
8

Bruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.

Full text
Abstract:
Diagnostic radiology is a medical specialty that is primarily devoted to the diagnostic process, centered on the interpretation of medical images. This book reviews the high level of uncertainty inherent to radiological interpretation and the overlap that exists between the uncertainty of the process and what might be considered “error.” There is also a great deal of variability inherent in the physical and technological aspects of the imaging process itself. The information in diagnostic images is subtly encoded, with a broad range of “normal” that usually overlaps the even broader range of “abnormal.” Image interpretation thus blends technology, medical science, and human intuition. To develop their skillset, radiologists train intensively for years, and most develop a remarkable level of expertise. But radiology itself remains a fallible human endeavor, one involving complex neurophysiological and cognitive processes employed under a range of conditions and generally performed under time pressure. This book highlights the human experience of error. A taxonomy of error is presented, along with a theoretical classification of error types based on the underlying causes and an extensive discussion of potential error-reduction strategies. The relevant perceptual science, cognitive science, and imaging science are reviewed. A chapter addresses the issue of accountability for error, including peer review, regulatory oversight/accreditation, and malpractice litigation. The potential impact of artificial intelligence, including the use of machine learning and deep-learning algorithms, to reduce human error and improve radiologists’ efficiency is also explored.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Causal machine learning"

1

Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Causal Discovery." In Encyclopedia of Machine Learning, 159. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_102.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Schölkopf, Bernhard. "Causality for Machine Learning." In Probabilistic and Causal Inference, 765–804. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3501714.3501755.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kambadur, Prabhanjan, Aurélie C. Lozano, and Ronny Luss. "Temporal Causal Modeling." In Financial Signal Processing and Machine Learning, 41–66. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118745540.ch4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Goudet, Olivier, Diviyan Kalainathan, Michèle Sebag, and Isabelle Guyon. "Learning Bivariate Functional Causal Models." In Cause Effect Pairs in Machine Learning, 101–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21810-2_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Almeida, Diogo Moitinho de. "Pattern-Based Causal Feature Extraction." In Cause Effect Pairs in Machine Learning, 321–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21810-2_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Tsai, Kao-Tai. "Causal Inference and Matching." In Machine Learning for Knowledge Discovery with R, 173–96. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003205685-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Klopotek, Mieczyslaw A. "Learning belief network structure from data under causal insufficiency." In Machine Learning: ECML-94, 379–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_78.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Hernández-Lobato, Daniel, Pablo Morales-Mombiela, David Lopez-Paz, and Alberto Suárez. "Non-linear Causal Inference Using Gaussianity Measures." In Cause Effect Pairs in Machine Learning, 257–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21810-2_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Duangsoithong, Rakkrit, and Terry Windeatt. "Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers." In Ensembles in Machine Learning Applications, 97–115. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22910-7_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Toloubidokhti, Maryam, Ryan Missel, Xiajun Jiang, Niels Otani, and Linwei Wang. "Neural State-Space Modeling with Latent Causal-Effect Disentanglement." In Machine Learning in Medical Imaging, 338–47. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21014-3_35.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Causal machine learning"

1

Cui, Peng, Zheyan Shen, Sheng Li, Liuyi Yao, Yaliang Li, Zhixuan Chu, and Jing Gao. "Causal Inference Meets Machine Learning." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3406460.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Karmakar, Somedip, Soumojit Guha Majumder, and Dhiraj Gangaraju. "Causal Inference and Causal Machine Learning with Practical Applications." In CODS-COMAD 2023: 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD). New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3570991.3571052.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Li, Ang, Suming J. Chen, Jingzheng Qin, and Zhen Qin. "Training Machine Learning Models With Causal Logic." In WWW '20: The Web Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3366424.3383415.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Singh, Amandeep, Kartik Hosanagar, and Amit Gandhi. "Machine Learning Instrument Variables for Causal Inference." In EC '20: The 21st ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3391403.3399466.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Syrgkanis, Vasilis, Greg Lewis, Miruna Oprescu, Maggie Hei, Keith Battocchi, Eleanor Dillon, Jing Pan, et al. "Causal Inference and Machine Learning in Practice with EconML and CausalML." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3470792.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Bozorgi, Zahra Dasht, Irene Teinemaa, Marlon Dumas, Marcello La Rosa, and Artem Polyvyanyy. "Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs." In 2020 2nd International Conference on Process Mining (ICPM). IEEE, 2020. http://dx.doi.org/10.1109/icpm49681.2020.00028.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Doong, Shing H., and Tean Q. Lee. "Causal driver detection with deviance information criterion." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580778.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Athey, Susan. "Machine Learning and Causal Inference for Policy Evaluation." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2783258.2785466.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Lu, Shuxia, and Jie Jiang. "Machine Learning Regressed Causal Inference for Discrete ANM." In 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI). IEEE, 2021. http://dx.doi.org/10.1109/cisai54367.2021.00137.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Rao, Dong-Ning, Zhi-Hua Jiang, and Yun-Fei Jiang. "Using Causal-Link Graphs to Detect Conflicts Among Goals." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370678.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Causal machine learning"

1

Chetverikov, Denis, Mert Demirer, Esther Duflo, Christian Hansen, Whitney K. Newey, and Victor Chernozhukov. Double machine learning for treatment and causal parameters. The IFS, September 2016. http://dx.doi.org/10.1920/wp.cem.2016.4916.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chernozhukov, Victor, Carlos Cinelli, Whitney Newey, Amit Sharma, and Vasilis Syrgkanis. Long Story Short: Omitted Variable Bias in Causal Machine Learning. Cambridge, MA: National Bureau of Economic Research, July 2022. http://dx.doi.org/10.3386/w30302.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bart, Cockx, Lehner Michael, and Bollens Joost. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. Maastricht University, Graduate School of Business and Economics, 2020. http://dx.doi.org/10.26481/umagsb.2020015.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bart, Cockx, Lehner Michael, and Bollens Joost. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. Research Centre for Education and the Labour Market, 2020. http://dx.doi.org/10.26481/umaror.2020006.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Cilliers, Jacobus, Eric Dunford, and James Habyarimana. What Do Local Government Education Managers Do to Boost Learning Outcomes? Research on Improving Systems of Education (RISE), March 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/064.

Full text
Abstract:
Decentralization reforms have shifted responsibility for public service delivery to local government, yet little is known about how their management practices or behavior shape performance. We conducted a comprehensive management survey of mid-level education bureaucrats and their staff in every district in Tanzania, and employ flexible machine learning techniques to identify important management practices associated with learning outcomes. We find that management practices explain 10 percent of variation in a district's exam performance. The three management practices most predictive of performance are: i) the frequency of school visits; ii) school and teacher incentives administered by the district manager; and iii) performance review of staff. Although the model is not causal, these findings suggest the importance of robust systems to motivate district staff, schools, and teachers, that include frequent monitoring of schools. They also show the importance of surveying subordinates of managers, in order to produce richer information on management practices.
APA, Harvard, Vancouver, ISO, and other styles
6

Rudner, Tim, and Helen Toner. Key Concepts in AI Safety: Interpretability in Machine Learning. Center for Security and Emerging Technology, March 2021. http://dx.doi.org/10.51593/20190042.

Full text
Abstract:
This paper is the third installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces interpretability as a means to enable assurance in modern machine learning systems.
APA, Harvard, Vancouver, ISO, and other styles
7

Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

Full text
Abstract:
Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
APA, Harvard, Vancouver, ISO, and other styles
8

Rduner, Tim G. J., and Helen Toner. Key Concepts in AI Safety: Specification in Machine Learning. Center for Security and Emerging Technology, December 2021. http://dx.doi.org/10.51593/20210031.

Full text
Abstract:
This paper is the fourth installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” outlined three categories of AI safety issues—problems of robustness, assurance, and specification—and the subsequent two papers described problems of robustness and assurance, respectively. This paper introduces specification as a key element in designing modern machine learning systems that operate as intended.
APA, Harvard, Vancouver, ISO, and other styles
9

Rudner, Tim, and Helen Toner. Key Concepts in AI Safety: Robustness and Adversarial Examples. Center for Security and Emerging Technology, March 2021. http://dx.doi.org/10.51593/20190041.

Full text
Abstract:
This paper is the second installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. The first paper in the series, “Key Concepts in AI Safety: An Overview,” described three categories of AI safety issues: problems of robustness, assurance, and specification. This paper introduces adversarial examples, a major challenge to robustness in modern machine learning systems.
APA, Harvard, Vancouver, ISO, and other styles
10

Rudner, Tim, and Helen Toner. Key Concepts in AI Safety: An Overview. Center for Security and Emerging Technology, March 2021. http://dx.doi.org/10.51593/20190040.

Full text
Abstract:
This paper is the first installment in a series on “AI safety,” an area of machine learning research that aims to identify causes of unintended behavior in machine learning systems and develop tools to ensure these systems work safely and reliably. In it, the authors introduce three categories of AI safety issues: problems of robustness, assurance, and specification. Other papers in this series elaborate on these and further key concepts.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Causal machine learning' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography