Bibliographies thématiques / Inferenza causale

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Littérature scientifique sur le sujet « Inferenza causale »

Auteur : Grafiati
Publié le 9 mars 2023
Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Inferenza causale"

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van der Laan, Mark J. "Causal Inference for a Population of Causally Connected Units." Journal of Causal Inference 2, no. 1 (2014): 13–74. http://dx.doi.org/10.1515/jci-2013-0002.

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AbstractSuppose that we observe a population of causally connected units. On each unit at each time-point on a grid we observe a set of other units the unit is potentially connected with, and a unit-specific longitudinal data structure consisting of baseline and time-dependent covariates, a time-dependent treatment, and a final outcome of interest. The target quantity of interest is defined as the mean outcome for this group of units if the exposures of the units would be probabilistically assigned according to a known specified mechanism, where the latter is called a stochastic intervention.
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Fougère, Denis, and Nicolas Jacquemet. "Causal Inference and Impact Evaluation." Economie et Statistique / Economics and Statistics, no. 510-511-512 (December 18, 2019): 181–200. http://dx.doi.org/10.24187/ecostat.2019.510t.1996.

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Zhou, Qingyang, Kangjie Lu, and Meng Xu. "Causally Consistent Normalizing Flow." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22974–81. https://doi.org/10.1609/aaai.v39i21.34460.

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Causal inconsistency arises when the underlying causal graphs captured by generative models like Normalizing Flows are inconsistent with those specified in causal models like Struct Causal Models. This inconsistency can cause unwanted issues including unfairness. Prior works to achieve causal consistency inevitably compromise the expressiveness of their models by disallowing hidden layers. In this work, we introduce a new approach: Causally Consistent Normalizing Flow (CCNF). To the best of our knowledge, CCNF is the first causally consistent generative model that can approximate any distribut
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Sober, Elliott, and David Papineau. "Causal Factors, Causal Inference, Causal Explanation." Aristotelian Society Supplementary Volume 60, no. 1 (1986): 97–136. http://dx.doi.org/10.1093/aristoteliansupp/60.1.97.

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Glymour, C., P. Spirtes, and R. Scheines. "Causal inference." Erkenntnis 35, no. 1-3 (1991): 151–89. http://dx.doi.org/10.1007/bf00388284.

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Rothman, Kenneth J., Stephan Lanes, and James Robins. "Causal Inference." Epidemiology 4, no. 6 (1993): 555. http://dx.doi.org/10.1097/00001648-199311000-00013.

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Kuang, Kun, Lian Li, Zhi Geng, et al. "Causal Inference." Engineering 6, no. 3 (2020): 253–63. http://dx.doi.org/10.1016/j.eng.2019.08.016.

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Staniloff, Howard M. "Causal Inference." JAMA: The Journal of the American Medical Association 261, no. 15 (1989): 2264. http://dx.doi.org/10.1001/jama.1989.03420150114051.

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Kim, Juyeon, Juyoung Hong, and Yukyung Choi. "Causal Inference for Modality Debiasing in Multimodal Emotion Recognition." Applied Sciences 14, no. 23 (2024): 11397. https://doi.org/10.3390/app142311397.

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Multimodal emotion recognition (MER) aims to enhance the understanding of human emotions by integrating visual, auditory, and textual modalities. However, previous MER approaches often depend on a dominant modality rather than considering all modalities, leading to poor generalization. To address this, we propose Causal Inference in Multimodal Emotion Recognition (CausalMER), which leverages counterfactual reasoning and causal graphs to capture relationships between modalities and reduce direct modality effects contributing to bias. This allows CausalMER to make unbiased predictions while bein
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Vandenbroucke, J. P. "Causal Inference is Necessary but Insufficient for Causal Inference." International Journal of Epidemiology 44, suppl_1 (2015): i53. http://dx.doi.org/10.1093/ije/dyv097.204.

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Thèses sur le sujet "Inferenza causale"

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HAMMAD, AHMED TAREK. "Tecniche di valutazione degli effetti dei Programmi e delle Politiche Pubbliche. L' approccio di apprendimento automatico causale." Doctoral thesis, Università Cattolica del Sacro Cuore, 2022. http://hdl.handle.net/10280/110705.

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L'analisi dei meccanismi causali è stata considerata in varie discipline come la sociologia, l’epidemiologia, le scienze politiche, la psicologia e l’economia. Questi approcci permettere di scoprire relazioni e meccanismi causali studiando il ruolo di una variabile di trattamento (come ad esempio una politica pubblica o un programma) su un insieme di variabili risultato di interesse o diverse variabili intermedie sul percorso causale tra il trattamento e le variabili risultato. Questa tesi si concentra innanzitutto sulla revisione e l'esplorazione di strategie alternative per indagare gli eff
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ROMIO, SILVANA ANTONIETTA. "Modelli marginali strutturali per lo studio dell'effetto causale di fattori di rischio in presenza di confondenti tempo dipendenti." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/8048.

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Uno degli obiettivi piu importanti della ricerca epidemiologica è quello di analizzare la relazione tra uno o più fattori di rischio ed un evento. Tali relazioni sono spesso complicate dalla presenza di confondenti, il cui concetto è estremamente complesso da formalizzare. Dal punto di vista dell'analisi causale, si dice che esiste confondimento quando la misura di associazione non coincide con quella di effetto corrispondente, cioè quando ad esempio il rischio relativo non coincide con il rischio relativo causale. Il problema è quindi quello di individuare i disegni e le ipotesi sulla base d
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Nguyên, Tri Long. "Inférence causale, modélisation prédictive et décision médicale." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT028.

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La prise de décision médicale se définit par le choix du traitement de la maladie, dans l’attente d’un résultat probable tentant de maximiser les bénéfices sur la santé du patient. Ce choix de traitement doit donc reposer sur les preuves scientifiques de son efficacité, ce qui renvoie à une problématique d’estimation de l’effet-traitement. Dans une première partie, nous présentons, proposons et discutons des méthodes d’inférence causale, permettant d’estimer cet effet-traitement par des approches expérimentales ou observationnelles. Toutefois, les preuves obtenues par ces méthodes fournissent
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Sun, Xiaohai. "Causal inference from statistical data /." Berlin : Logos-Verl, 2008. http://d-nb.info/988947331/04.

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LIU, DAYANG. "A Review of Causal Inference." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/44.

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In this report, I first review the evolution of ideas of causation as it relates to causal inference. Then I introduce two currently competing perspectives on this issue: the counterfactual perspective and the noncounterfactual perspective. The ideas of two statisticians, Donald B. Rubin, representing the counterfactual perspective, and A.P.Dawid, representing the noncounterfactual perspective are examined in detail and compared with the evolution of ideas of causality. The main difference between these two perspectives is that the counterfactual perspective is based on counterfactuals which c
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Sauley, Beau. "Three Essays in Causal Inference." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627659095905957.

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Liu, Dayang. "A review of causal inference." Worcester, Mass. : Worcester Polytechnic Institute, 2009. http://www.wpi.edu/Pubs/ETD/Available/etd-010909-121301/.

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Mahmood, Sharif. "Finding common support and assessing matching methods for causal inference." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/36190.

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Doctor of Philosophy<br>Department of Statistics<br>Michael J. Higgins<br>This dissertation presents an approach to assess and validate causal inference tools to es- timate the causal effect of a treatment. Finding treatment effects in observational studies is complicated by the need to control for confounders. Common approaches for controlling include using prognostically important covariates to form groups of similar units containing both treatment and control units or modeling responses through interpolation. This disser- tation proposes a series of new, computationally efficient methods to
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Guo, H. "Statistical causal inference and propensity analysis." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599787.

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Statistical causal inference from an observational study often requires adjustment for a possibly multi-dimensional covariate, where there is a need for dimension reduction. Propensity score analysis (Rosenbaum and Rubin 1983) is a popular approach to such reduction. This thesis addresses causal inference within Dawid’s decision-theoretic framework, where studies of “sufficient covariate” and its properties are essential. The role of a propensity variable, obtained from “treatment-sufficient reduction”, is illustrated and examined by a simple normal linear model. As propensity analysis is beli
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Fancsali, Stephen E. "Constructing Variables That Support Causal Inference." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/398.

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Livres sur le sujet "Inferenza causale"

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J, Rothman Kenneth, Lanes Stephan F, and Society for Epidemiologic Research (U.S.). Meeting, eds. Causal inference. Epidemiology Resources, 1988.

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Zhou, Xiao-Hua, and Jinzhu Jia, eds. Causal Inference. Springer Nature Singapore, 2025. http://dx.doi.org/10.1007/978-981-97-7812-6.

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Dayal, Vikram, and Anand Murugesan. Demystifying Causal Inference. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3905-3.

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Geffner, Hector, Rina Dechter, and Joseph Y. Halpern, eds. Probabilistic and Causal Inference. ACM, 2022. http://dx.doi.org/10.1145/3501714.

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Huynh, Van-Nam, Vladik Kreinovich, and Songsak Sriboonchitta, eds. Causal Inference in Econometrics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27284-9.

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Rohlfing, Ingo. Case Studies and Causal Inference. Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9781137271327.

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Fred, Wilson. Hume's defence of causal inference. University of Toronto Press, 1997.

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Li, Sheng, and Zhixuan Chu, eds. Machine Learning for Causal Inference. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35051-1.

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Jenkins, Jeffery A., ed. Causal Inference and American Political Development. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-74913-1.

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Lu, Rui. Feature Selection for High Dimensional Causal Inference. [publisher not identified], 2020.

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Chapitres de livres sur le sujet "Inferenza causale"

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Ye, Li. "Cause of Disease and Causal Inference." In Textbook of Clinical Epidemiology. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3622-9_9.

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Edwards, David. "Causal Inference." In Introduction to Graphical Modelling. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-0493-0_8.

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Glymour, C., P. Spirtes, and R. Scheines. "Causal Inference." In Erkenntnis Orientated: A Centennial Volume for Rudolf Carnap and Hans Reichenbach. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3490-3_9.

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Wasserman, Larry. "Causal Inference." In Springer Texts in Statistics. Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-21736-9_16.

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Dayal, Vikram. "Causal Inference." In Quantitative Economics with R. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2035-8_10.

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Etzioni, Ruth, Micha Mandel, and Roman Gulati. "Causal Inference." In Springer Texts in Statistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59889-1_8.

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Otsuka, Jun. "Causal Inference." In Thinking About Statistics. Routledge, 2022. http://dx.doi.org/10.4324/9781003319061-6.

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Gow, Ian D., and Tongqing Ding. "Causal inference." In Empirical Research in Accounting. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003456230-4.

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"Inference." In Causal Inference. Yale University Press, 2021. http://dx.doi.org/10.12987/9780300255881-039.

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Spirtes, Peter. "Common Cause In Causal Inference." In Philosophy of Statistics. Elsevier, 2011. http://dx.doi.org/10.1016/b978-0-444-51862-0.50025-3.

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Actes de conférences sur le sujet "Inferenza causale"

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Shaska, Joni, and Urbashi Mitra. "Neyman-Pearson Causal Inference." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619696.

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Shen, Yiwei. "Machine Learning in Causal Inference." In 2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP). IEEE, 2024. https://doi.org/10.1109/mlnlp63328.2024.10800203.

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Wu, Yupeng, and Wen Zhao. "Debiased Causal Inference for Sequential Recommendation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650048.

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Russo, Fabrizio, Anna Rapberger, and Francesca Toni. "Argumentative Causal Discovery." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/88.

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Causal discovery amounts to unearthing causal relationships amongst features in data. It is a crucial companion to causal inference, necessary to build scientific knowledge without resorting to expensive or impossible randomised control trials. In this paper, we explore how reasoning with symbolic representations can support causal discovery. Specifically, we deploy assumption-based argumentation (ABA), a well-established and powerful knowledge representation formalism, in combination with causality theories, to learn graphs which reflect causal dependencies in the data. We prove that our meth
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Srivastava, Kriti, Amitesh Sawarkar, Sachin Nawale, and Abhay Raj Agrawal. "Influence of Causal Inference For Crop Prediction." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725416.

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Zhang, Mingtong, Nady Slam, Zixiang Zhang, and Jingrong Wang. "Causal discovery and inference for urban fire." In Ninth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), edited by Pierluigi Siano and Wenbing Zhao. SPIE, 2024. http://dx.doi.org/10.1117/12.3033508.

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Qiu, Ruihong, Sen Wang, Zhi Chen, Hongzhi Yin, and Zi Huang. "CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation." In MM '21: ACM Multimedia Conference. ACM, 2021. http://dx.doi.org/10.1145/3474085.3475266.

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Glenski, Maria, and Svitlana Volkova. "Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community." In Proceedings of the First Workshop on Causal Inference and NLP. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.cinlp-1.7.

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Fytas, Panagiotis, Georgios Rizos, and Lucia Specia. "What Makes a Scientific Paper be Accepted for Publication?" In Proceedings of the First Workshop on Causal Inference and NLP. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.cinlp-1.4.

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Tan, Fiona Anting, Devamanyu Hazarika, See-Kiong Ng, Soujanya Poria, and Roger Zimmermann. "Causal Augmentation for Causal Sentence Classification." In Proceedings of the First Workshop on Causal Inference and NLP. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.cinlp-1.1.

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Rapports d'organisations sur le sujet "Inferenza causale"

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Costa Dias, Monica. Causal Inference in Economics. The IFS, 2012. http://dx.doi.org/10.1920/ps.ifs.2024.0841.

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Finkelstein, Amy, and Nathaniel Hendren. Welfare Analysis Meets Causal Inference. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27640.

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Bernheim, B. Douglas, Daniel Björkegren, Jeffrey Naecker, and Michael Pollmann. Causal Inference from Hypothetical Evaluations. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w29616.

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Gelman, Andrew, and Guido Imbens. Why ask Why? Forward Causal Inference and Reverse Causal Questions. National Bureau of Economic Research, 2013. http://dx.doi.org/10.3386/w19614.

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Lee, Sokbae (Simon), and Sung Jae Jun. Causal inference in case-control studies. The IFS, 2020. http://dx.doi.org/10.1920/wp.cem.2020.1920.

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Wooldridge, Jeffrey. Causal Inference with Cross-Sectional Data. Instats Inc., 2023. http://dx.doi.org/10.61700/utpyiijdiwlgi469.

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This workshop covers the potential outcomes approach to identification and estimation of causal (or treatment) effects in several situations that arise in various empirical research fields. The settings include unconfounded treatment assignment (with randomized assignment as a special case), confounded assignment with instrumental variables, and regression discontinuity designs. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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Wooldridge, Jeffrey. Causal Inference with Cross-Sectional Data. Instats Inc., 2023. http://dx.doi.org/10.61700/jfl18ze5ckjn0469.

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This workshop covers the potential outcomes approach to identification and estimation of causal (or treatment) effects in several situations that arise in various empirical research fields. The settings include unconfounded treatment assignment (with randomized assignment as a special case), confounded assignment with instrumental variables, and regression discontinuity designs. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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James, Richard J. E., Hyungseo Kim, Lucy Hitcham, and Richard J. Tunney. Causal inference methods in gambling research. Greo Evidence Insights, 2023. https://doi.org/10.33684/2024.004.

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The purpose of this project was to review and utilise methods from other disciplines in the social sciences in order to be able make stronger causal claims using crosssectional gambling data such as gambling prevalence studies. We focused on the question of whether there is a causal relationship between specific gambling products and individual gambling harms, specifically problem gambling. There has been an existing literature that has looked at this issue, but fails to control for selection biases on engagement with specific gambling behaviours. We reviewed and used three approaches: propens
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Sianesi, Barbara. Propensity score matching and causal inference. The IFS, 2009. http://dx.doi.org/10.1920/ps.ifs.2024.1048.

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Raudenbush, Stephen. Hierarchical Linear Models (HLM) and Multilevel Causal Inference. Instats Inc., 2023. http://dx.doi.org/10.61700/6mi8hginiy8rh469.

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This seminar introduces you to the theory and practice of multilevel modeling, and the logic of causal inference at multiple levels of analysis. The first day introduces you to two-level and three-level analysis, with an emphasis on how to build and interpret HLMs with a theoretically-rigorous foundation to guide estimation and inference. The second day considers how modern methods of causal inference can be applied to multilevel experimental and quasi-experimental designs. The new updated version of the HLM software will be used to illustrate model building and inference in a hands-on way. An
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