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Articles de revues sur le sujet « Inferenza causale »

Auteur : Grafiati
Publié le 9 mars 2023
Mis à jour le 11 mars 2023

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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. Causal effects of interest are defined as contrasts of the mean of the unit-specific outcomes under different stochastic interventions one wishes to evaluate. This covers a large range of estimation problems from independent units, independent clusters of units, and a single cluster of units in which each unit has a limited number of connections to other units. The allowed dependence includes treatment allocation in response to data on multiple units and so called causal interference as special cases. We present a few motivating classes of examples, propose a structural causal model, define the desired causal quantities, address the identification of these quantities from the observed data, and define maximum likelihood based estimators based on cross-validation. In particular, we present maximum likelihood based super-learning for this network data. Nonetheless, such smoothed/regularized maximum likelihood estimators are not targeted and will thereby be overly bias w.r.t. the target parameter, and, as a consequence, generally not result in asymptotically normally distributed estimators of the statistical target parameter.To formally develop estimation theory, we focus on the simpler case in which the longitudinal data structure is a point-treatment data structure. We formulate a novel targeted maximum likelihood estimator of this estimand and show that the double robustness of the efficient influence curve implies that the bias of the targeted minimum loss-based estimation (TMLE) will be a second-order term involving squared differences of two nuisance parameters. In particular, the TMLE will be consistent if either one of these nuisance parameters is consistently estimated. Due to the causal dependencies between units, the data set may correspond with the realization of a single experiment, so that establishing a (e.g. normal) limit distribution for the targeted maximum likelihood estimators, and corresponding statistical inference, is a challenging topic. We prove two formal theorems establishing the asymptotic normality using advances in weak-convergence theory. We conclude with a discussion and refer to an accompanying technical report for extensions to general longitudinal data structures.
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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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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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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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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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10

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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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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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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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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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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15

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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16

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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17

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 identificación de relaciones causales en epidemiología social.
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18

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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19

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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20

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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21

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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22

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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23

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 associations are or are not due to causal effects. Since recommending treatments that do not have beneficial causal effects will not improve health, causal inference can advance the practice of EBM. The purpose of this article is to familiarise clinicians with some of the concepts and terminology that are being used in the field of causal inference, including graphical diagrams known as ‘causal directed acyclic graphs’. In order to demonstrate some of the links between causal inference methods and clinical treatment decision-making, we use a clinical vignette of assessing treatments to lower cardiovascular risk. As the field of causal inference advances, clinicians familiar with the methods and terminology will be able to improve their adherence to the principles of EBM by distinguishing causal effects of treatment from results due to non-causal associations that may be a source of bias.
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24

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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25

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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26

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

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30

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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31

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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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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33

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 minimize this. Results: Key concepts and methods described include directed acyclic graphs, quantitative bias analysis, Mendelian randomization, and potential outcomes approaches which include propensity scores, g methods, and causal mediation. Conclusions: We provide a brief overview of some contemporary epidemiological methods that are beginning to be used in physical activity research. Adoption of these methods will help build a stronger body of evidence for the health benefits of physical activity.
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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 aspects of the issue, such as emerging trends and historical predictors. Epidemiologists will need to address 2 key aspects. The first is changing patterns in religious and spiritual identification. Specifically, how do traditional mechanisms (e.g., social support) hold up as explanations for religion-health associations now that more people identify as spiritual but not religious and more people are not attending religious services in physical buildings? The second is incorporation of place into causal inference designs. Specifically, how do we establish causal inference for associations between area-level constructs of the religious environment (e.g., denomination-specific church membership/adherence rates) and individual- and population-level health outcomes?
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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 psychopathology-related outcomes including depression, anxiety, psychological distress, schizophrenia, substance abuse/antisocial behaviour, and smoking initiation. The most commonly examined risk factors in the reviewed Mendelian randomisation studies were smoking, alcohol use and body mass index. In most cases, risk factors were strongly associated with psychopathology-related outcomes in conventional analyses but Mendelian randomisation indicated that these associations were unlikely to be causal. However, Mendelian randomisation analyses showed that both smoking and homocysteine plasma levels may be causally linked with schizophrenia. We discuss possible reasons for these diverging results between conventional and Mendelian randomisation analyses and outline future directions for progressing research in ways that maximise the potential for identifying targets for intervention.
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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 the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.
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37

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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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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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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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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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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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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Cox, D. R. "Causal Inference Without Counterfactuals: Comment." Journal of the American Statistical Association 95, no. 450 (2000): 424. http://dx.doi.org/10.2307/2669378.

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Casella, George, and Stephen P. Schwartz. "Causal Inference Without Counterfactuals: Comment." Journal of the American Statistical Association 95, no. 450 (2000): 425. http://dx.doi.org/10.2307/2669379.

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Pearl, Judea. "Causal Inference Without Counterfactuals: Comment." Journal of the American Statistical Association 95, no. 450 (2000): 428. http://dx.doi.org/10.2307/2669380.

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Robins, James M., and Sander Greenland. "Causal Inference Without Counterfactuals: Comment." Journal of the American Statistical Association 95, no. 450 (2000): 431. http://dx.doi.org/10.2307/2669381.

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Rubin, Donald B. "Causal Inference Without Counterfactuals: Comment." Journal of the American Statistical Association 95, no. 450 (2000): 435. http://dx.doi.org/10.2307/2669382.

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Shafer, Glenn. "Causal Inference Without Counterfactuals: Comment." Journal of the American Statistical Association 95, no. 450 (2000): 438. http://dx.doi.org/10.2307/2669383.

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Wasserman, Larry. "Causal Inference Without Counterfactuals: Comment." Journal of the American Statistical Association 95, no. 450 (2000): 442. http://dx.doi.org/10.2307/2669384.

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Dawid, A. P. "Causal Inference Without Counterfactuals: Rejoinder." Journal of the American Statistical Association 95, no. 450 (2000): 444. http://dx.doi.org/10.2307/2669385.

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