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Livres sur le sujet « Inferenza causale »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 11 mars 2023

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1

J, Rothman Kenneth, Lanes Stephan F et Society for Epidemiologic Research (U.S.). Meeting, dir. Causal inference. Chestnut Hill, MA : Epidemiology Resources, 1988.

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2

Geffner, Hector, Rina Dechter et Joseph Y. Halpern, dir. Probabilistic and Causal Inference. New York, NY, USA : ACM, 2022. http://dx.doi.org/10.1145/3501714.

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3

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

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4

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

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5

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

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6

Lu, Rui. Feature Selection for High Dimensional Causal Inference. [New York, N.Y.?] : [publisher not identified], 2020.

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7

Hirshberg, David Abraham. Minimax-inspired Semiparametric Estimation and Causal Inference. [New York, N.Y.?] : [publisher not identified], 2018.

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8

B, Willett John, dir. Methods matter : Improving causal inference in educational research. New York, NY : Oxford University Press, 2010.

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9

Bennett, Magdalena. Three Essays on Causal Inference for Observational Studies. [New York, N.Y.?] : [publisher not identified], 2020.

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10

Best, Henning, et Christof Wolf. The SAGE Handbook of Regression Analysis and Causal Inference. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom : SAGE Publications Ltd, 2014. http://dx.doi.org/10.4135/9781446288146.

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11

Prancan, Kathi, dir. Experimental and Quasi-Experimental Designs : For Generalized Causal Inference. Boston, USA : Houghton Mifflin Company, 2001.

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12

Sherri, Rose, dir. Targeted learning : Causal inference for observational and experimental data. New York : Springer, 2011.

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13

Leavitt, Thomas. Design-based, Bayesian Causal Inference for the Social-Sciences. [New York, N.Y.?] : [publisher not identified], 2021.

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14

Zubizarreta, José R., Elizabeth A. Stuart, Dylan S. Small et Paul R. Rosenbaum. Handbook of Matching and Weighting Adjustments for Causal Inference. Boca Raton : Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003102670.

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15

Jung, Minsoo. An Investigation of the Causal Inference between Epidemiology and Jurisprudence. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7862-0.

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16

Gelman, Andrew, et Xiao-Li Meng, dir. Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives. Chichester, UK : John Wiley & Sons, Ltd, 2004. http://dx.doi.org/10.1002/0470090456.

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17

Averitt, Amelia Jean. Machine Learning Methods for Causal Inference with Observational Biomedical Data. [New York, N.Y.?] : [publisher not identified], 2020.

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18

Cunningham, Scott. Causal Inference. Yale University Press, 2021. http://dx.doi.org/10.12987/9780300255881.

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19

Causal Inference. Taylor & Francis Group, 2019.

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20

Causal Inference. MIT Press, 2023.

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21

Kreinovich, Vladik, Van-Nam Huynh et Songsak Sriboonchitta. Causal Inference in Econometrics. Springer London, Limited, 2016.

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22

Elements of Causal Inference. The MIT Press, 2017.

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23

Causal Inference in Statistics. Wiely, 2016.

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24

Cunningham, Scott. Causal Inference : The Mixtape. Yale University Press, 2021.

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25

Kreinovich, Vladik, Van-Nam Huynh et Songsak Sriboonchitta. Causal Inference in Econometrics. Springer, 2015.

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26

Kreinovich, Vladik, Van-Nam Huynh et Songsak Sriboonchitta. Causal Inference in Econometrics. Springer, 2018.

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27

Causal Inference : The Mixtape. mixtape.scunning.com, 2021.

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28

Hernan, Miguel A., et James M. Robins. Causal Inference : What If. CRC Press, 2010.

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29

Counterfactuals and Causal Inference. Cambridge University Press, 2015.

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30

Fundamentals of Causal Inference. Taylor & Francis Group, 2021.

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31

Xiong, Momiao. Artificial Intelligence and Causal Inference. CRC Press LLC, 2022.

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32

Goodman, Steven N., et Jonathan M. Samet. Causal Inference in Cancer Epidemiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0007.

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Judgments about causality are central to the development of interventions intended to reduce exposure to risk factors that cause cancer. Because causation is not directly observable in medicine, scientists and philosophers have had to develop sets of constructs and heuristics that define “cause” operationally. The criteria in this framework, often attributed to the British medical statistician Sir Austin Bradford Hill or to the 1964 Report of the US Surgeon General on tobacco, include consistency, strength of association, specificity, temporality, coherence/plausibility/analogy, biological gradient, and experiment. This chapter reviews these criteria in depth and considers the challenges of applying them to population research on cancer. It discusses the concepts of causation in the context of the multistage nature of cancer, the “counterfactual” notion of causation, the component cause model for understanding diseases with multiple causes, and the “weight of the evidence” approach for integrating information from multiple lines of research.

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33

Golan, Amos. Causal Inference via Constraint Satisfaction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0011.

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In this chapter I introduce a number of ideas connected to causal inference that are inherently connected to info-metrics. In the context of this chapter, causal inference means the causality inferred from the available information. I begin by introducing and examining nonmonotonic and default logics, which were developed to deal with extremely high conditional probabilities. Other facets of info-metrics and causal inference are then discussed. I also show the direct effect of the complete set of input information on the inferred solution. I conclude the chapter with a detailed Markov example providing a more traditional approach to causal inference, developed within the info-metrics framework. The example builds on the notion of exogeneity and demonstrates that the info-metrics framework provides a simple way of incorporating additional exogenous information, thereby opening the way for empirical testing of causal inference. A short summary of the notion of “pure” causality is also provided.

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34

Rogowski, Jon C., et Betsy Sinclair. Causal Inference in Political Networks. Sous la direction de Jennifer Nicoll Victor, Alexander H. Montgomery et Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.6.

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Though scholars have developed an increasingly rich set of research findings regarding the structure of political networks, identifying causal associations between these networks and political outcomes of interest presents a variety of challenges. Addressing these challenges is especially important given the prominence of networks in theories of individual and collective behavior. This chapter uses the framework of the Neyman-Rubin causal model (potential outcomes framework) to discuss challenges to identification researchers face when studying how networks affect political outcomes. It then describes a set of strategies researchers can employ to address these challenges, including suggestions for best practices in the context of both observational and experimental research designs.

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35

Xiong, Momiao. Artificial Intelligence and Causal Inference. Taylor & Francis Group, 2022.

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36

Kelleher, Adam. Causal Inference for Data Scientists. Pearson Education, Limited, 2019.

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37

Xiong, Momiao. Artificial Intelligence and Causal Inference. Taylor & Francis Group, 2022.

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38

Causal Inference from Statistical Data. Berlin, Germany : Logos-Verlag Berlin, 2008.

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39

Xiong, Momiao. Artificial Intelligence and Causal Inference. Taylor & Francis Group, 2022.

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40

Xiong, Momiao. Artificial Intelligence and Causal Inference. Taylor & Francis Group, 2022.

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41

Brumback, Babette A. Fundamentals of Causal Inference : With R. Taylor & Francis Group, 2021.

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42

Manzo, Gianluca, dir. Agent‐based Models and Causal Inference. Wiley, 2022. http://dx.doi.org/10.1002/9781119704492.

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43

Levin, Ines, et Betsy Sinclair. Causal Inference with Complex Survey Designs. Sous la direction de Lonna Rae Atkeson et R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.4.

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This article discusses methods that combine survey weighting and propensity score matching to estimate population average treatment effects. Beginning with an overview of causal inference techniques that incorporate data from complex surveys and the usefulness of survey weights, it then considers approaches for incorporating survey weights into three matching algorithms, along with their respective methodologies: nearest-neighbor matching, subclassification matching, and propensity score weighting. It also presents the results of a Monte Carlo simulation study that illustrates the benefits of incorporating survey weights into propensity score matching procedures, as well as the problems that arise when survey weights are ignored. Finally, it explores the differences between population-based inferences and sample-based inferences using real-world data from the 2012 panel of The American Panel Survey (TAPS). The article highlights the impact of social media usage on political participation, when such impact is not actually apparent in the target population.

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44

Brumback, Babette A. Fundamentals of Causal Inference with R. Taylor & Francis Group, 2021.

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45

Jewell, Nicholas P., Madelyn Glymour et Judea Pearl. Causal Inference in Statistics : A Primer. Wiley & Sons, Incorporated, John, 2016.

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46

Brumback, Babette A. Fundamentals of Causal Inference : With R. Taylor & Francis Group, 2021.

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47

Jewell, Nicholas P., Madelyn Glymour et Judea Pearl. Causal Inference in Statistics : A Primer. Wiley & Sons, Incorporated, John, 2016.

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48

Manzo, Gianluca. Agent-Based Models and Causal Inference. Wiley & Sons, Incorporated, John, 2021.

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49

Manzo, Gianluca. Agent-Based Models and Causal Inference. Wiley & Sons, Limited, John, 2021.

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50

Jewell, Nicholas P., Madelyn Glymour et Judea Pearl. Causal Inference in Statistics : A Primer. Wiley & Sons, Incorporated, John, 2016.

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