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Journal articles on the topic 'Inferenza causale'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Aiello, Allison E., and Lawrence W. Green. "Introduction to the Symposium: Causal Inference and Public Health." Annual Review of Public Health 40, no. 1 (2019): 1–5. http://dx.doi.org/10.1146/annurev-publhealth-111918-103312.

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Assessing the extent to which public health research findings can be causally interpreted continues to be a critical endeavor. In this symposium, we invited several researchers to review issues related to causal inference in social epidemiology and environmental science and to discuss the importance of external validity in public health. Together, this set of articles provides an integral overview of the strengths and limitations of applying causal inference frameworks and related approaches to a variety of public health problems, for both internal and external validity.

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12

Mealli, Fabrizia. "Causal Inference Perspectives." Observational Studies 8, no. 2 (2022): 105–8. http://dx.doi.org/10.1353/obs.2022.0011.

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13

Tchetgen Tchetgen, Eric J. "Causal Inference Perspectives." Observational Studies 8, no. 2 (2022): 109–14. http://dx.doi.org/10.1353/obs.2022.0012.

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14

Ebrahim, S. "Improving causal inference." International Journal of Epidemiology 42, no. 2 (2013): 363–66. http://dx.doi.org/10.1093/ije/dyt058.

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15

Dunning, Thad. "Improving Causal Inference." Political Research Quarterly 61, no. 2 (2008): 282–93. http://dx.doi.org/10.1177/1065912907306470.

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16

Ferrando, Josep. "La inferencia causal." FMC - Formación Médica Continuada en Atención Primaria 12, no. 3 (2005): 189–90. http://dx.doi.org/10.1016/s1134-2072(05)71201-9.

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17

Bochman, Alexander, and Dov M. Gabbay. "Causal dynamic inference." Annals of Mathematics and Artificial Intelligence 66, no. 1-4 (2012): 231–56. http://dx.doi.org/10.1007/s10472-012-9319-5.

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18

He, Miao, Weixi Gu, Ying Kong, Lin Zhang, Costas J. Spanos, and Khalid M. Mosalam. "CausalBG: Causal Recurrent Neural Network for the Blood Glucose Inference With IoT Platform." IEEE Internet of Things Journal 7, no. 1 (2020): 598–610. http://dx.doi.org/10.1109/jiot.2019.2946693.

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19

Rodríguez-Villamizar, Laura Andrea. "Inferencia causal en epidemiología." Revista de Salud Pública 19, no. 3 (2017): 409–15. http://dx.doi.org/10.15446/rsap.v19n3.66180.

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En este ensayo, que corresponde a la segunda sesión del Seminario interuniversidades de programas de salud pública del I semestre de 2017, se revisó inicialmente de manera breve el desarrollo histórico de la definición de causa para comprender el desarrollo del pensamiento y de los modelos de causalidad. Posteriormente, se presentaron los fundamentos teóricos que sustentan la identificación de relaciones causales y los modelos y métodos de análisis disponibles. Finalmente, se presentaron algunas conclusiones respecto a las fortalezas y limitaciones que ofrece el análisis contrafactual en la id

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20

Hern??n, Miguel A., and James M. Robins. "Instruments for Causal Inference." Epidemiology 17, no. 4 (2006): 360–72. http://dx.doi.org/10.1097/01.ede.0000222409.00878.37.

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21

Michael J. Costa. "Hume and Causal Inference." Hume Studies 12, no. 2 (1986): 141–59. http://dx.doi.org/10.1353/hms.2011.0477.

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22

Holland, Paul W. "Statistics and Causal Inference." Journal of the American Statistical Association 81, no. 396 (1986): 945–60. http://dx.doi.org/10.1080/01621459.1986.10478354.

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23

Pearce, N. "Concepts of Causal Inference." International Journal of Epidemiology 44, suppl_1 (2015): i70. http://dx.doi.org/10.1093/ije/dyv097.256.

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24

Eberhardt, Frederick, and Richard Scheines. "Interventions and Causal Inference." Philosophy of Science 74, no. 5 (2007): 981–95. http://dx.doi.org/10.1086/525638.

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25

Stovitz, Steven D., and Ian Shrier. "Causal inference for clinicians." BMJ Evidence-Based Medicine 24, no. 3 (2019): 109–12. http://dx.doi.org/10.1136/bmjebm-2018-111069.

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Evidence-based medicine (EBM) calls on clinicians to incorporate the ‘best available evidence’ into clinical decision-making. For decisions regarding treatment, the best evidence is that which determines the causal effect of treatments on the clinical outcomes of interest. Unfortunately, research often provides evidence where associations are not due to cause-and-effect, but rather due to non-causal reasons. These non-causal associations may provide valid evidence for diagnosis or prognosis, but biased evidence for treatment effects. Causal inference aims to determine when we can infer that as

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26

Gagliardi, Luigi. "Prediction and causal inference." Acta Paediatrica 98, no. 12 (2009): 1890–92. http://dx.doi.org/10.1111/j.1651-2227.2009.01540.x.

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27

Herbert, Robert D. "Research Note: Causal inference." Journal of Physiotherapy 66, no. 4 (2020): 273–77. http://dx.doi.org/10.1016/j.jphys.2020.07.010.

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28

Shams, Ladan, and Ulrik R. Beierholm. "Causal inference in perception." Trends in Cognitive Sciences 14, no. 9 (2010): 425–32. http://dx.doi.org/10.1016/j.tics.2010.07.001.

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29

Riguzzi, Fabrizio, Giuseppe Cota, Elena Bellodi, and Riccardo Zese. "Causal inference in cplint." International Journal of Approximate Reasoning 91 (December 2017): 216–32. http://dx.doi.org/10.1016/j.ijar.2017.09.007.

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30

Li, Fan, and Constantine E. Frangakis. "Polydesigns and Causal Inference." Biometrics 62, no. 2 (2005): 343–51. http://dx.doi.org/10.1111/j.1541-0420.2005.00494.x.

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31

&NA;. "Instruments for Causal Inference." Epidemiology 25, no. 1 (2014): 164. http://dx.doi.org/10.1097/ede.0000000000000035.

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32

Dawid, A. P. "Causal Inference without Counterfactuals." Journal of the American Statistical Association 95, no. 450 (2000): 407–24. http://dx.doi.org/10.1080/01621459.2000.10474210.

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33

Ray, Kolyan, and Aad van der Vaart. "Semiparametric Bayesian causal inference." Annals of Statistics 48, no. 5 (2020): 2999–3020. http://dx.doi.org/10.1214/19-aos1919.

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34

Holland, Paul W., Clark Glymour, and Clive Granger. "STATISTICS AND CAUSAL INFERENCE*." ETS Research Report Series 1985, no. 2 (1985): i—72. http://dx.doi.org/10.1002/j.2330-8516.1985.tb00125.x.

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35

Shah, Abhin. "Data-Rich Causal Inference." ACM SIGMETRICS Performance Evaluation Review 51, no. 3 (2024): 54–57. http://dx.doi.org/10.1145/3639830.3639851.

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Brief Biography: Abhin Shah is a final-year Ph.D. student in the department of Electrical Engineering and Computer Science atMIT, where he is a recipient ofMIT's Jacobs Presidential Fellowship. He has interned with Google Research (2021) and IBM Research (2020).

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36

Young, Jessica G. "Story-led Causal Inference." Epidemiology 35, no. 3 (2024): 289–94. http://dx.doi.org/10.1097/ede.0000000000001704.

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37

Shukla, Deepa. "Beyond the Average: Personalized Causal Inference in Econometrics with Machine Learning." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 937–41. http://dx.doi.org/10.21275/sr24210172433.

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38

Kishore Chakrabarty, Pradipta. "Causal Inference in Agentic AI: Bridging Explainability and Dynamic Decision Making." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 2112–17. https://doi.org/10.21275/sr25424081718.

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39

Lynch, Brigid M., Suzanne C. Dixon-Suen, Andrea Ramirez Varela, et al. "Approaches to Improve Causal Inference in Physical Activity Epidemiology." Journal of Physical Activity and Health 17, no. 1 (2020): 80–84. http://dx.doi.org/10.1123/jpah.2019-0515.

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Background: It is not always clear whether physical activity is causally related to health outcomes, or whether the associations are induced through confounding or other biases. Randomized controlled trials of physical activity are not feasible when outcomes of interest are rare or develop over many years. Thus, we need methods to improve causal inference in observational physical activity studies. Methods: We outline a range of approaches that can improve causal inference in observational physical activity research, and also discuss the impact of measurement error on results and methods to mi

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40

Chen, Xi. "Causal Inference Analysis of Family Educational Expectations and Students’ Cognitive Abilities ——Based on CEPS Data." SHS Web of Conferences 174 (2023): 01012. http://dx.doi.org/10.1051/shsconf/202317401012.

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Based on data from the China Education Panel Survey (CEPS), this paper used the SCM (structural causal model, SCM) to study the causal inferences between family educational expectations and students’ cognitive abilities. The SCM model, originally proposed by Judy Perl, is effective in inferring causal relationships between variables to help policy makers develop appropriate policies and measures. We found that there is a direct causal relationship between family educational expectations and students’ cognitive ability. Family characteristics variables such as: family economic level, family cul

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41

Ransome, Yusuf. "Religion, Spirituality, and Health: New Considerations for Epidemiology." American Journal of Epidemiology 189, no. 8 (2020): 755–58. http://dx.doi.org/10.1093/aje/kwaa022.

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Abstract Religion and spirituality are important social determinants that drive public health practice. The field of epidemiology has played a vital role in answering long-standing questions about whether religion is causally associated with health and mortality. As epidemiologists spark new conversations (e.g., see Kawachi (Am J Epidemiol. (https://doi.org/10.1093/aje/kwz204)) and Chen and VanderWeele (Am J Epidemiol. 2018;187(11):2355–2364)) about methods (e.g., outcomes-wide analysis) used to establish causal inference between religion and health, epidemiologists need to engage with other a

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42

Pingault, Jean-Baptiste, Charlotte A. M. Cecil, Joseph Murray, Marcus R. Munafò, and Essi Viding. "Causal Inference in Psychopathology: A Systematic Review of Mendelian Randomisation Studies Aiming to Identify Environmental Risk Factors for Psychopathology." Psychopathology Review a4, no. 1 (2016): 4–25. http://dx.doi.org/10.5127/pr.038115.

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Psychopathology represents a leading cause of disability worldwide. Effective interventions need to target risk factors that are causally related to psychopathology. In order to distinguish between causal and spurious risk factors, it is critical to account for environmental and genetic confounding. Mendelian randomisation studies use genetic variants that are independent from environmental and genetic confounders in order to strengthen causal inference. We conducted a systematic review of studies (N = 19) using Mendelian randomisation to examine the causal role of putative risk factors for ps

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43

Harnessing, Data Science to Optimize House Construction: A. Holistic Approach to Cost Reduction and Efficiency Enhancement. "Harnessing Data Science to Optimize House Construction: A Holistic Approach to Cost Reduction and Efficiency Enhancement." European Journal of Advances in Engineering and Technology 7, no. 1 (2020): 49–54. https://doi.org/10.5281/zenodo.13831732.

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The construction industry, particularly in residential housing, faces ongoing challenges in cost management and efficiency optimization. This paper presents a comprehensive data science framework for optimizing house construction processes, focusing on cost reduction and efficiency enhancement. By leveraging advanced analytics, machine learning, and predictive modeling techniques, we propose a novel approach to address key areas of construction management, including material procurement, labor allocation, project scheduling, and quality control. Our methodology integrates diverse data sources,

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44

Yao, Liuyi, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. "A Survey on Causal Inference." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (2021): 1–46. http://dx.doi.org/10.1145/3444944.

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Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under

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45

VanderWeele, Tyler J. "Constructed Measures and Causal Inference." Epidemiology 33, no. 1 (2021): 141–51. http://dx.doi.org/10.1097/ede.0000000000001434.

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46

Dorfman, Hayley M., Momchil S. Tomov, Bernice Cheung, Dennis Clarke, Samuel J. Gershman, and Brent L. Hughes. "Causal Inference Gates Corticostriatal Learning." Journal of Neuroscience 41, no. 32 (2021): 6892–904. http://dx.doi.org/10.1523/jneurosci.2796-20.2021.

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47

Suter, Glenn W. "Causal inference for ecological impairments." Frontiers in Ecology and the Environment 7, no. 3 (2009): 129. http://dx.doi.org/10.1890/09.wb.009.

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48

Greenland, Sander. "Randomization, Statistics, and Causal Inference." Epidemiology 1, no. 6 (1990): 421–29. http://dx.doi.org/10.1097/00001648-199011000-00003.

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49

Halloran, M. Elizabeth, and Claudio J. Struchiner. "Causal Inference in Infectious Diseases." Epidemiology 6, no. 2 (1995): 142–51. http://dx.doi.org/10.1097/00001648-199503000-00010.

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50

Glass, Thomas A., Steven N. Goodman, Miguel A. Hernán, and Jonathan M. Samet. "Causal Inference in Public Health." Annual Review of Public Health 34, no. 1 (2013): 61–75. http://dx.doi.org/10.1146/annurev-publhealth-031811-124606.

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