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Books on the topic 'Inferenza causale'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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1

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

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2

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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3

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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4

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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5

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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6

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

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7

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

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8

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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9

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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10

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

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11

Kilcioglu, Cinar. Essays on Cloud Pricing and Causal Inference. [publisher not identified], 2016.

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12

Makela, Susanna. Essays in Cluster Sampling and Causal Inference. [publisher not identified], 2018.

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13

Hirshberg, David Abraham. Minimax-inspired Semiparametric Estimation and Causal Inference. [publisher not identified], 2018.

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14

Bennett, Magdalena. Three Essays on Causal Inference for Observational Studies. [publisher not identified], 2020.

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15

B, Willett John, ed. Methods matter: Improving causal inference in educational research. Oxford University Press, 2010.

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16

Best, Henning, and Christof Wolf. The SAGE Handbook of Regression Analysis and Causal Inference. SAGE Publications Ltd, 2014. http://dx.doi.org/10.4135/9781446288146.

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17

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

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18

Causal Inference. MIT Press, 2023.

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19

Causal Inference. MIT Press, 2023.

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20

Causal Inference. Taylor & Francis Group, 2019.

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21

Kreinovich, Vladik, Van-Nam Huynh, and 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: What If. Taylor & Francis Group, 2023.

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24

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

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25

Causal Inference: What If. Taylor & Francis Group, 2024.

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26

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

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27

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

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28

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

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29

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

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30

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

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31

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

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32

Causal Inference in Statistics. Wiely, 2016.

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33

Counterfactuals and Causal Inference. Cambridge University Press, 2015.

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34

Facure, Matheus. Causal Inference in Python: Applying Causal Inference in the Tech Industry. O'Reilly Media, Incorporated, 2023.

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35

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

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36

Goodman, Steven N., and 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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Abstract:
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 gra
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37

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
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38

Rogowski, Jon C., and Betsy Sinclair. Causal Inference in Political Networks. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and 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 d
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39

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

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40

Villa, Alex Ruiz de. Causal Inference for Data Science. Manning Publications Co. LLC, 2023.

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41

Fang, Yixin. Causal Inference in Pharmaceutical Statistics. CRC Press LLC, 2024.

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42

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

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43

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

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44

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

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45

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

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46

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

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47

Chu, Zhixuan. Machine Learning for Causal Inference. Springer International Publishing AG, 2023.

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48

Diamond, Alexis J. Essays on causal inference in observational studies. 2008.

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49

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

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50

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

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