Analyse causale et méthodes quantitatives : une introduction avec R, Stata et SPSS

L'analyse causale est une des tâches principales du scientifique. Un criminologue évalue l'effet d'une sentence sur la probabilité qu'un condamné récidive. Une économiste mesure l'effet de la discrimination raciale sur les perspectives d'emploi d'un immigrant. Un politologue étudie l'effet des médias sociaux sur la popularité des partis d'extrême droite. Une spécialiste du marketing jauge l'effet d'une campagne publicitaire sur les choix des consommateurs. Malheureusement, démontrer l'existence de telles relations est difficile, puisque de nombreux phénomènes sociaux ou physiques sont fortement associés, sans être liés par une relation de cause à effet. La distinction entre association et causalité est une des pierres d'assise de la démarche scientifique. Pourtant, cette distinction est souvent ignorée dans la vie de tous les jours, quand des arguments causaux sont défendus sur la base de simples observations descriptives. Cette différence est aussi passée sous silence dans la formation méthodologique que plusieurs étudiants reçoivent à l'université. Trop souvent, les manuels de méthodes quantitatives ignorent la question causale, ou recommandent d'interpréter les résultats d'un modèle statistique en termes causaux, alors qu'ils sont corrélationnels. Pour remédier à ce problème, ce livre offre une introduction intégrée aux méthodes quantitatives et à l'analyse causale. En plus de présenter les outils nécessaires pour exécuter des analyses statistiques, il offre un cadre théorique simple et rigoureux pour interpréter les résultats de ces analyses. Ce cadre théorique permet d'identifier les conditions qui doivent être réunies afin que l'interprétation causale de nos résultats soit justifiée

The Limits of Foreign Aid Diplomacy: How Bureaucratic Design Shapes Aid Distribution

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113691/1/isqu12191-sup-0001-appendixS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/113691/2/isqu12191.pd

The EU and the politics of blacklisting tax havens

Blacklisting is a widespread and controversial instrument designed to induce tax havens to change their domestic policies. Since the Global Financial Crisis, several international organizations like the OECD and the EU have published tax haven blacklists, but these lists have been widely criticized as a flawed policy tool. In this paper, we use a mixed methods approach to explore the political rationale behind the establishment of the EU blacklist, and the causal mechanisms through which the list was expected to exert influence over governments in tax havens. First, we draw on process-tracing and expert interviews to establish that the list was less designed as an effective policy tool to induce compliance with international standards, and more as a political impetus to shape the overall problem definition, strengthen the Commissions bargaining position, and influence public opinion. Second, we conduct a survey experiment in Switzerland to determine if using a blacklist to name-and-shame and threaten economic sanctions can effectively shape public opinion in a low-tax jurisdiction. We find that “naming-and-shaming” and “economic threat” have a statistically significant effect on public opinion in favor of tax reform, but that this effect is modest

Algorithm Selection Framework for Cyber Attack Detection

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2

Towards an enhanced understanding of aid policy reform: learning from the French case

Major overhauls of aid policies and institutions are comparatively rare. When they happen, they are usually ascribed to pressures arising from outside donor agencies. Where internal forces for change are identified, the focus is on field operatives rather than political entrepreneurs based in donor head offices. This article homes in on the role of the political entrepreneur and shows how this actor can help effect top‐down reforms to overseas development assistance. It does so by combining a political entrepreneurship perspective with a broader theorisation of policy change, historical institutionalism, and applying this innovative framework to French aid reforms over the years (2001–2010) when Jean‐Michel Severino was Managing Director of the Agence Française de Développement. It finds that, although historical institutionalism can explain the broad direction of French changes in terms of “structural factors” such as exogenous shocks and new institutional configurations, it struggles to account for incremental shifts and the emergence of “new” ideas. Political entrepreneurship addresses these issues through its emphasis on individual human agents and their operational and ideational strategies. It concludes that this relatively parsimonious framework could provide an enhanced understanding of other reforms in the international development field and beyond

modelsummary: Data and Model Summaries in R

modelsummary is a package to summarize data and statistical models in R. It supports over one hundred types of models out-of-the-box, and allows users to report the results of those models side-by-side in a table, or in coefficient plots. It makes it easy to execute common tasks such as computing robust standard errors, adding significance stars, and manipulating coefficient and model labels. Beyond model summaries, the package also includes a suite of tools to produce highly flexible data summary tables, such as dataset overviews, correlation matrices, (multi-level) cross-tabulations, and balance tables (also known as "Table 1"). The appearance of the tables produced by modelsummary can be customized using external packages such as kableExtra, gt, flextable, or huxtable; the plots can be customized using ggplot2. Tables can be exported to many output formats, including HTML, LaTeX, Text/Markdown, Microsoft Word, Powerpoint, Excel, RTF, PDF, and image files. Tables and plots can be embedded seamlessly in rmarkdown, knitr, or Sweave dynamic documents. The modelsummary package is designed to be simple, robust, modular, and extensible