Relevant bibliographies by topics / Inferenza causale

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Inferenza causale'

Author: Grafiati
Published: 9 March 2023
Last updated: 11 March 2023

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Journal articles on the topic "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 (March 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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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 (July 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 (July 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 (November 1993): 555. http://dx.doi.org/10.1097/00001648-199311000-00013.

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Kuang, Kun, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, and Zhichao Jiang. "Causal Inference." Engineering 6, no. 3 (March 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 (April 21, 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 (September 23, 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 (April 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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Mealli, Fabrizia. "Causal Inference Perspectives." Observational Studies 8, no. 2 (October 2022): 105–8. http://dx.doi.org/10.1353/obs.2022.0011.

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Dissertations / Theses on the topic "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 effetti causali e gli effetti di mediazione multipli utilizzando algoritmi di apprendimento automatico (Machine Learning) che si sono dimostrati particolarmente adatti per rispondere a domande di ricerca in contesti complessi caratterizzati dalla presenza di relazioni non lineari. In secondo luogo, la tesi fornisce due esempi empirici in cui due algoritmi di Machine Learning, ovvero Generalized Random Foresta e Multiple Additive Regression Trees, vengono utilizzati per tenere conto di importanti variabili di controllo nell'inferenza causale seguendo un approccio “data-driven”.
The analysis of causal mechanisms has been considered in various disciplines such as sociology, epidemiology, political science, psychology and economics. These approaches allow uncovering causal relations and mechanisms by studying the role of a treatment variable (such as a policy or a program) on a set of outcomes of interest or different intermediates variables on the causal path between the treatment and the outcome variables. This thesis first focuses on reviewing and exploring alternative strategies to investigate causal effects and multiple mediation effects using Machine Learning algorithms which have been shown to be particularly suited for assessing research questions in complex settings with non-linear relations. Second, the thesis provides two empirical examples where two Machine Learning algorithms, namely the Generalized Random Forest and Multiple Additive Regression Trees, are used to account for important control variables in causal inference in a data-driven way. By bridging a fundamental gap between causality and advanced data modelling, this work combines state of the art theories and modelling techniques.
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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 delle quali è possibile calcolare l'effetto causale oggetto di studio. Ad esempio, gli studi clinici controllati randomizzati sono nati con lo scopo di minimizzare l'influenza di errori sistematici nella misurazione dell'effetto di un fattore di rischio su di un outcome. Inoltre in questi studi le misure di associazione risultano essere uguali a quelle di effetto (causali). Negli studi osservazionali lo scenario diventa più complesso per la presenza di una o più variabili che possono alterare o 'confondere' la relazione d'interesse poichè lo sperimentatore non può in alcun modo intervenire sulle covariate osservate né sull'outcome. Di particolare interesse risulta quindi l'identificazione di metodi che permettano di risolvere il problema del confondimento. Il problema è particolarmente complesso nello studio dell'effetto causale di un fattore di rischio in presenza di confondenti tempo dipendenti e cioè una variabile che, condizionatamente alla storia di esposizione pregressa è un predittore sia dell'outcome che dell'esposizione successiva. Nel presente lavoro è stato studiato un importante problema di sanità pubblica come quello di esplorare l'esistenza di una relazione causale tra abitudine al fumo e diminuzione dell'indice di massa corporea (body mass index - BMI) considerando come confondente tempo dipendente lo stesso BMI misurato al tempo precedente, utilizzando un modello marginale strutturale per misure ripetute avendo a disposizione i dati relativi ad una coorte di studenti svedesi (coorte BROMS). L'elevata numerosita di tale coorte e l'accuratezza e tipologia dei dati raccolti la rendono particolarente adatta allo studio di fenomeni dinamici comportamentali caratteristici dell'adolescenza. Dallo studio emerge come l'effetto causale cumulato del fumo di sigaretta sulla riduzione del BMI è significativo solo nelle donne, con una stima del parametro relativo all'interazione tra l'esposizione al fumo e genere pari a 0.322 (p-value < 0.001) mentre la stima del parametro relativo al consumo cumulato di sigarette nei maschi è non signicativo e pari a 0.053 (p-value pari a 0.464). I risultati ottenuti sono consistenti con quanto riportato in studi precedenti.
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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 une information sur l’effet-traitement uniquement à l’échelle de la population globale, et non à l’échelle de l’individu. Connaître le devenir probable du patient est essentiel pour adapter une décision clinique. Nous présentons donc, dans une deuxième partie, l’approche par modélisation prédictive, qui a permis une avancée en médecine personnalisée. Les modèles prédictifs fournissent au clinicien une information pronostique pour son patient, lui permettant ensuite le choix d’adapter le traitement. Cependant, cette approche a ses limites, puisque ce choix de traitement repose encore une fois sur des preuves établies en population globale. Dans une troisième partie, nous proposons donc une méthode originale d’estimation de l’effet-traitement individuel, en combinant inférence causale et modélisation prédictive. Dans le cas où un traitement est envisagé, notre approche permettra au clinicien de connaître et de comparer d’emblée le pronostic de son patient « avant traitement » et son pronostic « après traitement ». Huit articles étayent ces approches
Medical decision-making is defined by the choice of treatment of illness, which attempts to maximize the healthcare benefit, given a probable outcome. The choice of a treatment must be therefore based on a scientific evidence. It refers to a problem of estimating the treatment effect. In a first part, we present, discuss and propose causal inference methods for estimating the treatment effect using experimental or observational designs. However, the evidences provided by these approaches are established at the population level, not at the individual level. Foreknowing the patient’s probability of outcome is essential for adapting a clinical decision. In a second part, we present the approach of predictive modeling, which provided a leap forward in personalized medicine. Predictive models give the patient’s prognosis at baseline and then let the clinician decide on treatment. This approach is therefore limited, as the choice of treatment is still based on evidences stated at the overall population level. In a third part, we propose an original method for estimating the individual treatment effect, by combining causal inference and predictive modeling. Whether a treatment is foreseen, our approach allows the clinician to foreknow and compare both the patient’s prognosis without treatment and the patient’s prognosis with treatment. Within this thesis, we present a series of eight articles
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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 cannot be observed even in principle but the noncounterfactual perspective only relies on observables. I describe the definition of causes and causal inference methods under both perspectives, and I illustrate the application of the two types of methods by specific examples. Finally, I explore various controversies on these two perspectives.
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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
Department of Statistics
Michael J. Higgins
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 improve the analysis of observational studies. Treatment effects are only reliably estimated for a subpopulation under which a common support assumption holds—one in which treatment and control covariate spaces overlap. Given a distance metric measuring dissimilarity between units, a graph theory is used to find common support. An adjacency graph is constructed where edges are drawn between similar treated and control units to determine regions of common support by finding the largest connected components (LCC) of this graph. The results show that LCC improves on existing methods by efficiently constructing regions that preserve clustering in the data while ensuring interpretability of the region through the distance metric. This approach is extended to propose a new matching method called largest caliper matching (LCM). LCM is a version of cardinality matching—a type of matching used to maximize the number of units in an observational study under a covariate balance constraint between treatment groups. While traditional cardinality matching is an NP-hard, LCM can be completed in polynomial time. The performance of LCM with other five popular matching methods are shown through a series of Monte Carlo simulations. The performance of the simulations is measured by the bias, empirical standard deviation and the mean square error of the estimates under different treatment prevalence and different distributions of covariates. The formed matched samples improve estimation of the population treatment effect in a wide range of settings, and suggest cases in which certain matching algorithms perform better than others. Finally, this dissertation presents an application of LCC and matching methods on a study of the effectiveness of right heart catheterization (RHC) and find that clinical outcomes are significantly worse for patients that undergo RHC.
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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 believed to reduce bias and improve precision, both population-based and sample-based linear regressions have been implemented, with adjustments for the multivariate covariate and for a scalar propensity variable. Theoretical illustrations are then verified by simulation results. In addition, propensity analysis in a non-linear model: logistic regression is also discussed, followed by the investigation of the augmented inverse probability weighted (AIPW) estimator, which is a combination of a response model and a propensity model. It is found that, in the linear regression with homoscedasticity, propensity variable analysis results in exactly the same estimated causal effect as that from multivariate linear regression, for both population and sample. It is claimed that adjusting for an estimated propensity variable yields better precision than the true propensity variable, which is proved to not be universally valid. The AIPW estimator has the property of “Double robustness” and it is possible to improve the precision given that the propensity model is correctly specified.
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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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Books on the topic "Inferenza causale"

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

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Geffner, Hector, Rina Dechter, and Joseph Y. Halpern, eds. Probabilistic and Causal Inference. New York, NY, USA: 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. Cham: 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. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9781137271327.

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

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

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

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

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

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Best, Henning, and 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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Book chapters on the topic "Inferenza causale"

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Edwards, David. "Causal Inference." In Introduction to Graphical Modelling, 219–43. New York, NY: 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, 151–89. Dordrecht: 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, 251–62. New York, NY: 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, 153–223. Singapore: 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, 149–72. Cham: 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, 144–71. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003319061-6.

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Granger, C. W. J. "Causal Inference." In The New Palgrave Dictionary of Economics, 1440–43. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_688.

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Granger, C. W. J. "Causal Inference." In The New Palgrave Dictionary of Economics, 1–4. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_688-1.

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Mitchell, Renée J. "Causal Inference." In Twenty-one Mental Models That Can Change Policing, 107–13. New York: Routledge, 2021. http://dx.doi.org/10.4324/9780367481520-24.

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Dablander, Fabian, and Riet van Bork. "Causal Inference." In Network Psychometrics with R, 213–32. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003111238-16.

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Conference papers on the topic "Inferenza causale"

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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. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474085.3475266.

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Syrgkanis, Vasilis, Greg Lewis, Miruna Oprescu, Maggie Hei, Keith Battocchi, Eleanor Dillon, Jing Pan, et al. "Causal Inference and Machine Learning in Practice with EconML and CausalML." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3470792.

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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. Stroudsburg, PA, USA: 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. Stroudsburg, PA, USA: 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. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.cinlp-1.1.

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Low, Daniel, Kelly Zuromski, Daniel Kessler, Satrajit S. Ghosh, Matthew K. Nock, and Walter Dempsey. "It’s quality and quantity: the effect of the amount of comments on online suicidal posts." In Proceedings of the First Workshop on Causal Inference and NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.cinlp-1.8.

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Founta, Antigoni, and Lucia Specia. "A Survey of Online Hate Speech through the Causal Lens." In Proceedings of the First Workshop on Causal Inference and NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.cinlp-1.6.

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Tierney, Graham, and Alexander Volfovsky. "Sensitivity Analysis for Causal Mediation through Text: an Application to Political Polarization." In Proceedings of the First Workshop on Causal Inference and NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.cinlp-1.5.

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Wang, Zhao, Kai Shu, and Aron Culotta. "Enhancing Model Robustness and Fairness with Causality: A Regularization Approach." In Proceedings of the First Workshop on Causal Inference and NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.cinlp-1.3.

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Keith, Katherine, Douglas Rice, and Brendan O’Connor. "Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects." In Proceedings of the First Workshop on Causal Inference and NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.cinlp-1.2.

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Reports on the topic "Inferenza causale"

1

Finkelstein, Amy, and Nathaniel Hendren. Welfare Analysis Meets Causal Inference. Cambridge, MA: National Bureau of Economic Research, August 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. Cambridge, MA: National Bureau of Economic Research, December 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. Cambridge, MA: National Bureau of Economic Research, November 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, May 2020. http://dx.doi.org/10.1920/wp.cem.2020.1920.

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Baum-Snow, Nathaniel, and Fernando Ferreira. Causal Inference in Urban and Regional Economics. Cambridge, MA: National Bureau of Economic Research, October 2014. http://dx.doi.org/10.3386/w20535.

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Kuroki, Manabu, and Judea Pearl. Measurement Bias and Effect Restoration in Causal Inference. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada557455.

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Fafchamps, Marcel, and Julien Labonne. Using Split Samples to Improve Inference about Causal Effects. Cambridge, MA: National Bureau of Economic Research, January 2016. http://dx.doi.org/10.3386/w21842.

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Belloni, Alexandre, Victor Chernozhukov, Ivan Fernandez-Val, and Christian Hansen. Program evaluation and causal inference with high-dimensional data. The Institute for Fiscal Studies, March 2016. http://dx.doi.org/10.1920/wp.cem.2016.1316.

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Tan, Zhiqiang, Tobias Gerhard, and Baoluo Sun. Developing New Methods for Causal Inference in Observational Studies. Patient-Centered Outcomes Research Institute (PCORI), November 2021. http://dx.doi.org/10.25302/11.2021.me.151132740.

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Card, David, David S. Lee, Zhuan Pei, and Andrea Weber. Inference on Causal Effects in a Generalized Regression Kink Design. W.E. Upjohn Institute, January 2015. http://dx.doi.org/10.17848/wp15-218.

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