Academic literature on the topic 'Causal machine learning'
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Journal articles on the topic "Causal machine learning"
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 textWalker, 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 textZhao, 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 textGoodman, 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 textHuenermund, 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 textJung, 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 textArti, 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 textSasou, 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 textZhao, 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 textCrown, 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 textDissertations / Theses on the topic "Causal machine learning"
Moffett, Jeffrey P. "Applying Causal Models to Dynamic Difficulty Adjustment in Video Games." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/320.
Full textBethard, 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 textBalsa, 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 textbeen 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.
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 textThis 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.
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 textLash, Michael Timothy. "Optimizing outcomes via inverse classification." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6602.
Full textKaiser, 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 textMiranda, 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 textHazan, 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 textEsta 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.
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 textBooks on the topic "Causal machine learning"
Borchardt, Gary C. Thinking between the lines: Computers and the comprehension of causal descriptions. Cambridge, Mass: MIT Press, 1994.
Find full textWaldmann, Michael R. Causal Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.1.
Full textElements of Causal Inference. The MIT Press, 2017.
Find full textSekhon, 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 textBertocci, 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 textAlonso 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 textStoddard 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 textBruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.
Full textBook chapters on the topic "Causal machine learning"
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 textSchölkopf, 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 textKambadur, 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 textGoudet, 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 textAlmeida, 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 textTsai, 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 textKlopotek, 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 textHernández-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 textDuangsoithong, 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 textToloubidokhti, 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 textConference papers on the topic "Causal machine learning"
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 textKarmakar, 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 textLi, 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 textSingh, 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 textSyrgkanis, 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 textBozorgi, 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 textDoong, 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 textAthey, 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 textLu, 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 textRao, 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 textReports on the topic "Causal machine learning"
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 textChernozhukov, 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 textBart, 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 textBart, 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 textCilliers, 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 textRudner, 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 textPerdigão, 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 textRduner, 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 textRudner, 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 textRudner, 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.
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