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Thèses sur le sujet « Inferenza causale »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

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 eff

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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 d

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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

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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 c

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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<br>Department of Statistics<br>Michael J. Higgins<br>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

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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 beli

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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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Morrissey, Edward R. "Bayesian inference of causal gene networks." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/45732/.

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Genes do not act alone, rather they form part of large interacting networks with certain genes regulating the activity of others. The structure of these networks is of great importance as it can produce emergent behaviour, for instance, oscillations in the expression of network genes or robustness to uctuations. While some networks have been studied in detail, most networks underpinning biological processes have not been fully characterised. Elucidating the structure of these networks is of paramount importance to understand these biological processes. With the advent of whole-genome gene expr

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Lu, Jiannan. "On Causal Inference for Ordinal Outcomes." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:23845443.

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This dissertation studies the problem of causal inference for ordinal outcomes. Chapter 1 focuses on the sharp null hypothesis of no treatment effect on all experimental units, and develops a systematic procedure for closed-form construction of sequences of alternative hypotheses in increasing orders of their departures from the sharp null hypothesis. The resulted construction procedure helps assessing the powers of randomization tests with ordinal outcomes. Chapter 2 proposes two new causal parameters, i.e., the probabilities that the treatment is beneficial and strictly beneficial for the ex

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Murray, Eleanor Jane. "Agent-Based Models for Causal Inference." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:27201721.

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Sound clinical decision making requires evidence-based estimates of the impact of different treatment strategies. In the absence of randomized trials, two potential approaches are agent-based models (ABMs) and the parametric g-formula. Although these methods are mathematically similar, they have generally been considered in isolation. In this dissertation, we bridge the gap between ABMs and the parametric g-formula, in order to improve the use of ABMs for causal inference. In Chapter 1, we describe bias that can occur when ABM inputs or estimates are extrapolated to new populations, and demon

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Shpitser, Ilya. "Complete identification methods for causal inference." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1708387761&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Lam, Patrick Kenneth. "Estimating Individual Causal Effects." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11150.

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Most empirical work focuses on the estimation of average treatment effects (ATE). In this dissertation, I argue for a different way of thinking about causal inference by estimating individual causal effects (ICEs). I argue that focusing on estimating ICEs allows for a more precise and clear understanding of causal inference, reconciles the difference between what the researcher is interested in and what the researcher estimates, allows the researcher to explore and discover treatment effect heterogeneity, bridges the quantitative-qualitative divide, and allows for easy estimation of any othe

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Amjad, Muhammad Jehangir. "Sequential data inference via matrix estimation : causal inference, cricket and retail." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120190.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 185-193).<br>This thesis proposes a unified framework to capture the temporal and longitudinal variation across multiple instances of sequential data. Examples of such data include sales of a product over a period of time across

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Lin, Winston. "Essays on Causal Inference in Randomized Experiments." Thesis, University of California, Berkeley, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3593906.

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<p> This dissertation explores methodological topics in the analysis of randomized experiments, with a focus on weakening the assumptions of conventional models.</p><p> Chapter 1 gives an overview of the dissertation, emphasizing connections with other areas of statistics (such as survey sampling) and other fields (such as econometrics and psychometrics).</p><p> Chapter 2 reexamines Freedman's critique of ordinary least squares regression adjustment in randomized experiments. Using Neyman's model for randomization inference, Freedman argued that adjustment can lead to worsened asymptotic p

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Zajonc, Tristan. "Essays on Causal Inference for Public Policy." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10163.

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Effective policymaking requires understanding the causal effects of competing proposals. Relevant causal quantities include proposals' expected effect on different groups of recipients, the impact of policies over time, the potential trade-offs between competing objectives, and, ultimately, the optimal policy. This dissertation studies causal inference for public policy, with an emphasis on applications in economic development and education. The ﬁrst chapter introduces Bayesian methods for time-varying treatments that commonly arise in economics, health, and education. I present methods that a

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Brendel, Markus. "Essays on causal inference in corporate finance." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-180823.

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This dissertation work provides a kaleidoscope of alternative empirical estimation techniques while illuminating and challenging conventional approaches and established findings in the Corporate Finance literature. In particular, the observed „conglomerate discount“ and the effect of diversication and concentrated ownership on firm value are revisited in the course of my cumulated doctoral thesis. In doing so, the main emphasis lies on the inference of causation in the presence of endogeneity concerns, namely by considering potential distortions caused by unobserved heterogeneity, reverse caus

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Budhathoki, Kailash [Verfasser]. "Causal inference on discrete data / Kailash Budhathoki." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1226153801/34.

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Feller, Avi Isaac. "Essays in Causal Inference and Public Policy." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467344.

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This dissertation addresses statistical methods for understanding treatment effect variation in randomized experiments, both in terms of variation across pre-treatment covariates and variation across post-randomization intermediate outcomes. These methods are then applied to data from the National Head Start Impact Study (HSIS), a large-scale randomized evaluation of the Federally funded preschool program, which has become an important part of the policy debate in early childhood education. Chapter 2 proposes a randomization-based approach for testing for the presence of treatment effect vari

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Garcia, Horton Viviana. "Topics in Bayesian Inference for Causal Effects." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:23845483.

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This manuscript addresses two topics in Bayesian inference for causal effects. 1) Treatment noncompliance is frequent in clinical trials, and because the treatment actually received may be different from that assigned, comparisons between groups as randomized will no longer assess the effect of the treatment received. To address this complication, we create latent subgroups based on the potential outcomes of treatment received and focus on the subgroup of compliers, where under certain assumptions the estimands of causal effects of assignment can be interpreted as causal effects of receipt of

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Bailey, Delia Ruth Grigg Katz Jonathan N. "Essays on causal inference and political representation /." Diss., Pasadena, Calif. : California Institute of Technology, 2007. http://resolver.caltech.edu/CaltechETD:etd-05242007-154102.

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Tortù, Costanza. "Essays on causal inference and complex networks." Thesis, IMT Alti Studi Lucca, 2020. http://e-theses.imtlucca.it/323/1/Tort%C3%B9_phdthesis.pdf.

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This dissertation is a collection of articles that develop statistical methods for performing causal inference on network data. In bridging these two themes, causal inference and complex networks, the thesis develops four complementary methodological contributions in two main settings that often arise in network data: (i) both the treatment and the outcome are measured at the individual level but the treatment spills over through the network connections; (ii) both the treatment and outcomes are measured at dyadic level. In the first setting, it elaborates innovative techniques for ass

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García, Núñez Luis. "Econometría de evaluación de impacto." Economía, 2012. http://repositorio.pucp.edu.pe/index/handle/123456789/117180.

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In recent years the program evaluation methods have become very popular in applied microeconomics. However, the variety of these methods responds to specific problems, which are normally determined by the data available and the impact the researcher tries to measure. This paper summarizes the main methods in the current literature, emphasizing the assumptions under which the average treatment effect and the average treatment effect on the treated are identified. Additionally, after each section I briefly present some applications of these methods. This document is a didactic presentation for a

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Andric, Nikola. "Exploring Objective Causal Inference in Case-Noncase Studies under the Rubin Causal Model." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467481.

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Case-noncase studies, also known as case-control studies, are ubiquitous in epidemiology, where a common goal is to estimate the effect of an exposure on an outcome of interest. In many areas of application, such as policy-informing drug utilization research, this effect is inherently causal. Although logistic regression, the predominant method for analysis of case-noncase data, and other traditional methodologies, may provide associative insights, they are generally inappropriate for causal conclusions. As such, they fail to address the very essence of many epidemiological investigations that

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Echtermeyer, Christoph. "Causal pattern inference from neural spike train data." Thesis, St Andrews, 2009. http://hdl.handle.net/10023/843.

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Lundin, Mathias. "Sensitivity Analysis of Untestable Assumptions in Causal Inference." Doctoral thesis, Umeå universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-43239.

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This thesis contributes to the research field of causal inference, where the effect of a treatment on an outcome is of interest is concerned. Many such effects cannot be estimated through randomised experiments. For example, the effect of higher education on future income needs to be estimated using observational data. In the estimation, assumptions are made to make individuals that get higher education comparable with those not getting higher education, to make the effect estimable. Another assumption often made in causal inference (both in randomised an nonrandomised studies) is that the tre

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Ramsahai, Roland Ryan. "Causal inference with instruments and other supplementary variables." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:df2961da-0843-421f-8be4-66a92e6b0d13.

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Instrumental variables have been used for a long time in the econometrics literature for the identification of the causal effect of one random variable, B, on another, C, in the presence of unobserved confounders. In the classical continuous linear model, the causal effect can be point identified by studying the regression of C on A and B on A, where A is the instrument. An instrument is an instance of a supplementary variable which is not of interest in itself but aids identification of causal effects. The method of instrumental variables is extended here to generalised linear models, for whi

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Gong, Zhaojing. "Parametric Potential-Outcome Survival Models for Causal Inference." Thesis, University of Canterbury. Mathematics and Statistics, 2008. http://hdl.handle.net/10092/1803.

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Estimating causal effects in clinical trials is often complicated by treatment noncompliance and missing outcomes. In time-to-event studies, estimation is further complicated by censoring. Censoring is a type of missing outcome, the mechanism of which may be non-ignorable. While new estimates have recently been proposed to account for noncompliance and missing outcomes, few studies have specifically considered time-to-event outcomes, where even the intention-to-treat (ITT) estimator is potentially biased for estimating causal effects of assigned treatment. In this thesis, we develop a series

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Arcangeloni, Luca. "Causal Inference for Jamming Detection in Adverse Scenarios." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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The goal of this thesis is the development of an anti-jamming defense mechanism based on causal inference. The current state-of-the-art methods to compute causality, i.e., Granger Causality (GC), Transfer Entropy (TE) and Convergent Cross Mapping (CCM) are presented and they are used to detect the smart jammer into an appropriate simulation environment. The performances of the causality tools are evaluated, pointing out how the TE obtains the best results while the GC fails the detection of the intruder. The innovative CCM algorithm, instead, requires to function a deterministic structure of t

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Lu, Danni. "Representation Learning Based Causal Inference in Observational Studies." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102426.

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This dissertation investigates novel statistical approaches for causal effect estimation in observational settings, where controlled experimentation is infeasible and confounding is the main hurdle in estimating causal effect. As such, deconfounding constructs the main subject of this dissertation, that is (i) to restore the covariate balance between treatment groups and (ii) to attenuate spurious correlations in training data to derive valid causal conclusions that generalize. By incorporating ideas from representation learning, adversarial matching, generative causal estimation, and invarian

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Kovach, Matthew. "Causal Inference of Human Resources Key Performance Indicators." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1542361652897175.

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Lee, Joseph Jiazong. "Extensions of Randomization-Based Methods for Causal Inference." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463974.

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In randomized experiments, the random assignment of units to treatment groups justifies many of the traditional analysis methods for evaluating causal effects. Specifying subgroups of units for further examination after observing outcomes, however, may partially nullify any advantages of randomized assignment when data are analyzed naively. Some previous statistical literature has treated all post-hoc analyses homogeneously as entirely invalid and thus uninterpretable. Alternative analysis methods and the extent of the validity of such analyses remain largely unstudied. Here Chapter 1 prop

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Ding, Peng. "Exploring the Role of Randomization in Causal Inference." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467349.

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This manuscript includes three topics in causal inference, all of which are under the randomization inference framework (Neyman, 1923; Fisher, 1935a; Rubin, 1978). This manuscript contains three self-contained chapters. Chapter 1. Under the potential outcomes framework, causal effects are defined as comparisons between potential outcomes under treatment and control. To infer causal effects from randomized experiments, Neyman proposed to test the null hypothesis of zero average causal effect (Neyman’s null), and Fisher proposed to test the null hypothesis of zero individual causal effect (Fi

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Burauel, Patrick [Verfasser]. "Essays on Methods for Causal Inference / Patrick Burauel." Berlin : Freie Universität Berlin, 2020. http://d-nb.info/1218077816/34.

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MOSCELLI, GIUSEPPE. "Essays on causal inference and applied health economics." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2013. http://hdl.handle.net/2108/207907.

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Hajage, David. "Utilisation du score de propension et du score pronostique en pharmacoépidémiologie." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCC175/document.

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Les études observationnelles en pharmacoépidémiologie sont souvent mises en place pour évaluer un médicament mis sur le marché récemment ou concurrencé par de nombreuses alternatives thérapeutiques. Cette situation conduit à devoir évaluer l'effet d'un médicament dans une cohorte comprenant peu de sujets traités, c'est à dire une population où l'exposition d'intérêt est rare. Afin de prendre en compte les facteurs de confusion dans cette situation, certains auteurs déconseillent l'utilisation du score de propension au profit du score pronostique, mais cette recommandation ne s'appuie sur aucun

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Hamada, Sophie Rym. "Analyse de la prise en charge des patients traumatisés sévères dans le contexte français : processus de triage et processus de soin." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS572.

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La traumatologie est un problème de santé publique au troisième rang des années de vie perdues ajustées sur l’incapacité en France. L’investissement sanitaire et le volume de recherche qu’elle génère sont en deçà de ce que représente son impact sociétal. L’objet de ce travail de recherche était de plonger au cœur du parcours du patient traumatisé sévère pour en cibler trois problématiques clefs et tenter de répondre aux interrogations qu’elles génèrent.Les données utilisées provenaient essentiellement d’un observatoire de traumatologie lourde hospitalier (Traumabase®), régional et national, qu

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Häggström, Jenny. "Selection of smoothing parameters with application in causal inference." Doctoral thesis, Umeå universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-39614.

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This thesis is a contribution to the research area concerned with selection of smoothing parameters in the framework of nonparametric and semiparametric regression. Selection of smoothing parameters is one of the most important issues in this framework and the choice can heavily influence subsequent results. A nonparametric or semiparametric approach is often desirable when large datasets are available since this allow us to make fewer and weaker assumptions as opposed to what is needed in a parametric approach. In the first paper we consider smoothing parameter selection in nonparametric regr

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Waernbaum, Ingeborg. "Covariate selection and propensity score specification in causal inference." Doctoral thesis, Umeå : Umeå universitet, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1688.

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Oelrich, Oscar. "Causal Inference Using Propensity Score Matching in Clustered Data." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-225990.

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Propensity score matching is commonly used to estimate causal effects of treatments. However, when using data with a hierarchical structure, we need to take the multilevel nature of the data into account. In this thesis the estimation of propensity scores with multilevel models is presented to extend propensity score matching for use with multilevel data. A Monte Carlo simulation study is performed to evaluate several different estimators. It is shown that propensity score estimators ignoring the multilevel structure of the data are biased, while fixed effects models produce unbiased results.

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Geneletti, Sara Gisella. "Aspects of causal inference in a non-counterfactual framework." Thesis, University College London (University of London), 2005. http://discovery.ucl.ac.uk/1445505/.

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Since the mid 1970s and increasingly over the last decade, causal inference has generated interest and controversy in statistics. Mathematical frame works have been developed to make causal inference in fields ranging from epidemiology to social science. However, most frameworks rely on the existence of counterfactuals, and the assumptions that underpin them are not always made explicit. This thesis analyses such assumptions and proposes an alternative model. This is then used to tackle problems that have been formulated in counterfactual terms. The proposed framework is based on decision theo

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Ding, Jiacheng. "Causal Inference based Fault Localization for Python Numerical Programs." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1530904294580033.

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Sun, BaoLuo. "Semi-Parametric Methods for Missing Data and Causal Inference." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493594.

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In this dissertation, we propose methodology to account for missing data as well as a strategy to account for outcome heterogeneity. Missing data occurs frequently in empirical studies in health and social sciences, often compromising our ability to make accurate inferences. An outcome is said to be missing not at random (MNAR) if, conditional on the observed variables, the missing data mechanism still depends on the unobserved outcome. In such settings, identification is generally not possible without imposing additional assumptions. Identification is sometimes possible, however, if an exoge

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Béal, Jonas. "De la modélisation mécanistique des voies de signalisation dans le cancer à l’interprétation des modèles et de leurs apports : applications cliniques et évaluation statistique." Electronic Thesis or Diss., Université Paris sciences et lettres, 2020. https://theses.hal.science/tel-03188676.

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Au delà de ses mécanismes génétiques, le cancer peut être compris comme une maladie de réseaux qui résulte souvent de l’interaction entre différentes perturbations dans un réseau de régulation cellulaire. La dynamique de ces réseaux et des voies de signalisation associées est complexe et requiert des approches intégrées. Une d’entre elles est la conception de modèles dits mécanistiques qui traduisent mathématiquement la connaissance biologique des réseaux afin de pouvoir simuler le fonctionnement moléculaire des cancers informatiquement. Ces modèles ne traduisent cependant que les mécanismes g

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Liu, Jinzhong. "Bayesian Inference for Treatment Effect." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504803668961964.

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Majid, Asifa. "Language and causal understanding : there's something about Mary." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366213.

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Elling, Eva. "Effects of MIFID II on Stock Trade Volumes of Nasdaq Stockholm." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257510.

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Introducing new financial legislation to financial markets require caution to achieve the intended outcome. This thesis aims to investigate whether or not the newly installed revised Markets in Financial Instruments Directive- the MIFID II regulation - temporally influenced the trading stock volume levels of Nasdaq Stockholm during its introduction to the Swedish stock market. A first approach of a generalized Negative Binomial model is carried out on aggregated data, followed by an individual Fixed Effects model in an attempt to eliminate omitted variable bias caused by missing unobserved var

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Olsen, Catharina. "Causal inference and prior integration in bioinformatics using information theory." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209401.

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An important problem in bioinformatics is the reconstruction of gene regulatory networks from expression data. The analysis of genomic data stemming from high- throughput technologies such as microarray experiments or RNA-sequencing faces several difficulties. The first major issue is the high variable to sample ratio which is due to a number of factors: a single experiment captures all genes while the number of experiments is restricted by the experiment’s cost, time and patient cohort size. The second problem is that these data sets typically exhibit high amounts of noise.<p><p>Another impor

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