Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-23T14:11:23.990Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling configurational explanations

Published online by Cambridge University Press:  16 February 2021

Alessia Damonte Open the ORCID record for Alessia Damonte [Opens in a new window]
Alessia Damonte*
Affiliation:
Università degli Studi di Milano, Milan, Italy
*
Corresponding author. Email: alessia.damonte@unimi.it
Get access

Abstract

How can Qualitative Comparative Analysis contribute to causal knowledge? The article's answer builds on the shift from design to models that the Structural Causal Model framework has compelled in the probabilistic analysis of causation. From this viewpoint, models refine the claim that a ‘treatment’ has causal relevance as they specify the ‘covariates’ that make some units responsive. The article shows how QCA can establish configurational models of plausible ‘covariates’. It explicates the rationale, operations, and criteria that confer explanatory import to configurational models, then illustrates how the basic structures of the SCM can widen the interpretability of configurational solutions and deepen the dialogue among techniques.

Keywords

Explanationinus causationmediationpruningQualitative Comparative Analysisquasi-experimental designsStructural Causal Model framework
Type
Research Article
Information
Italian Political Science Review / Rivista Italiana di Scienza Politica , Volume 51 , Special Issue 2: Capturing Causation: Issues of Research Design in Political Science , July 2021 , pp. 182 - 197
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Società Italiana di Scienza Politica.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bareinboim, E and Pearl, J (2016) Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences 113, 7345–7352.CrossRefGoogle ScholarPubMed
Berg-Schlosser, D and De Meur, G (2009) Comparative research design: case and variable selection. In Rihoux, B and Ragin, CC (eds), Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques. London, UK: Sage, pp. 19–32.10.4135/9781452226569.n2CrossRefGoogle Scholar
Cartwright, N (2017) Causal powers. Why Humeans can't even be instrumentalist. In Jacobs, JD, ed. Causal Powers. Oxford, UK: Oxford University Press, pp. 9–23.CrossRefGoogle Scholar
Damonte, A (2017) Deploying administrative accountability to hinder systemic corruption: what do we know, and what can we expect? Rivista Italiana di Politiche Pubbliche 3, 417–441.Google Scholar
Damonte, A (2018) Gauging the import and essentiality of single conditions in standard configurational solutions. Sociological Methods & Research. OnlineFirst. doi.org/10.1177/0049124118794678.Google Scholar
Duşa, A (2018) QCA with R: A Comprehensive Resource. Cham, CH: Springer.Google Scholar
Dușa, A (2019) Critical tension: sufficiency and parsimony in QCA. Sociological Methods & Research. Online First. doi.org/10.1177/0049124119882456.CrossRefGoogle Scholar
Fiss, PC (2011) Building better causal theories: a fuzzy-set approach to typologies in organization research. The Academy of Management Journal 5, 393–420.Google Scholar
Goertz, G (2006) Assessing the trivialness, relevance, and relative importance of necessary or sufficient conditions in social science. Studies in Comparative International Development 41, 88–109.CrossRefGoogle Scholar
Goertz, G and Mahoney, J (2012) Concepts and measurement: ontology and epistemology. Social Science Information 51, 205–16.10.1177/0539018412437108CrossRefGoogle Scholar
Hájek, A (2011) Conditional probability. In Bandyopadhyay, PS and Forster, MR (eds). Philosophy of Statistics. Amsterdam, NL: North-Holland, pp. 99–135.CrossRefGoogle Scholar
Imbens, GW (2004) Nonparametric estimation of average treatment effects under exogeneity. Review of Economics and Statistics 86, 4–29.10.1162/003465304323023651CrossRefGoogle Scholar
King, G, Keohane, RO and Verba, S (1994) Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Kuroki, M and Pearl, J (2014) Measurement bias and effect restoration in causal inference. Biometrika 101, 423–37.10.1093/biomet/ast066CrossRefGoogle Scholar
Lazarsfeld, PF and Henry, NW (1968) Latent Structure Analysis. New York, NY: Houghton Mifflin Co.Google Scholar
Mackie, JL (1965) Causes and conditions. American Philosophical Quarterly, 2, 245–264.Google Scholar
Mackie, JL (1980) The Cement of the Universe. London, UK: Oxford University Press.CrossRefGoogle Scholar
Marx, A and Dusa, A (2011) Crisp-set qualitative comparative analysis (csQCA), contradictions and consistency benchmarks for model specification. Methodological Innovations Online, 6, 103–148.10.4256/mio.2010.0037CrossRefGoogle Scholar
Møller, J and Skaaning, SE (2019) Set-theoretic methods in democratization research: an evaluation of their uses and contributions. Democratization. 26, 78–96.CrossRefGoogle Scholar
Morgan, SL and Winship, C (2015) Counterfactuals and Causal Inference. New York, NY: Cambridge University Press.Google Scholar
Munck, G (2016) Assessing set-theoretic comparative methods: a tool for qualitative comparativists? Comparative Political Studies 49, 775–780.10.1177/0010414015626453CrossRefGoogle Scholar
Mungiu-Pippidi, A (2013) The Good, the Bad and the Ugly: Controlling Corruption in the European Union, http://anticorrp.eu/wp-content/uploads/2016/04/D3.4.1-The-Good-The-Bad-The-Ugly.pdf.Google Scholar
Oana, IE and Schneider, CQ (2018) Setmethods: an add-on R package for advanced QCA. The R Journal 10, 507–33.CrossRefGoogle Scholar
Ostrom, E (1998) A behavioral approach to the rational choice theory of collective action. American Political Science Review 92, 1–22.CrossRefGoogle Scholar
Pearl, J (2009) Causality: Models, Reasoning, and Inference. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Pearl, J and Mackenzie, D (2018) The Book of Why: The New Science of Cause and Effect. New York, NY: Basic Books.Google Scholar
Pearl, J and Paz, A (1987) Graphoids: a graph-based logic for reasoning about relevance relations. In Duboulay, B, Hogg, D and Steels, L (eds), Advances in Artificial Intelligence-II. Amsterdam, NL: North-Holland Publishing Co, pp. 357–363.Google Scholar
Pearl, J and Verma, T (1991) A theory of inferred causation. In Allena, J, Fikes, R and Sandewall, E (eds), Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference. San Mateo, CA: Morgan Kaufmann, pp. 441–452.Google Scholar
Ragin, CC (2000) Fuzzy-Set Social Science. Chicago, IL: University of Chicago Press.Google Scholar
Ragin, CC (2006) Set relations in social research: evaluating their consistency and coverage. Political Analysis, 14, 291–310.CrossRefGoogle Scholar
Ragin, CC (2008) Redesigning Social Inquiry: Fuzzy Sets and Beyond. Chicago, IL: University of Chicago Press.CrossRefGoogle Scholar
Ragin, CC (2014) The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies, 2nd Edn. Berkeley, CA: University of California Press.CrossRefGoogle Scholar
Ragin, CC and Fiss, PC (2017) Intersectional Inequality: Race, Class, Test Scores, and Poverty. Chicago, IL: University of Chicago Press.Google Scholar
Ramsey, FP (1929) General propositions and causality. In Mellor, DH (ed), (1990) F.P. Ramsey: Philosophical Papers. Cambridge, UK: Cambridge University Press, pp. 145–63.Google Scholar
Rihoux, B and De Meur, G (2009) Crisp-set qualitative comparative analysis (csQCA). In Rihoux, B and Ragin, CC (eds), Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques. London, UK: Sage, pp. 33–68.CrossRefGoogle Scholar
Rohlfing, I (2020) The choice between crisp and fuzzy sets in QCA and the ambiguous consequences for finding consistent set relations. Field Methods 32, 75–88.CrossRefGoogle Scholar
Rohlfing, I and Schneider, CQ (2013) Improving research on necessary conditions: formalized case selection for process tracing after QCA. Political Research Quarterly 66, 220–235.Google Scholar
Rosenbaum, PR and Rubin, DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55.CrossRefGoogle Scholar
Rubin, DB (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.CrossRefGoogle Scholar
Rubin, DB (1978) Bayesian Inference for causal effects: the role of randomization. The Annals of Statistics, 6, 34–58.CrossRefGoogle Scholar
Rubinson, C (2013) Contradictions in fsQCA. Quality & Quantity 47, 2847–2867.CrossRefGoogle Scholar
Salmon, WC (1989) Four Decades of Scientific Explanation. Pittsburgh, PA: University of Pittsburgh Press.Google Scholar
Sartori, G (1984) Guidelines for concept analysis. In Id, (ed), Social Science Concepts: A Systematic Analysis. Beverly Hills, CA: Sage, pp. 15–85.Google Scholar
Schneider, CQ (2019) Two-step QCA revisited: the necessity of context conditions. Quality and Quantity 53, 1109–1126.CrossRefGoogle Scholar
Schneider, CQ and Wagemann, C (2012) Set-Theoretic Methods for the Social Sciences. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Seawright, J (2019) Statistical analysis of democratization: a constructive critique. Democratization, 26, 21–39.CrossRefGoogle Scholar
Simon, H (1977) Models of Discovery. Dordrecht, NL: Reidel Publishing Company.CrossRefGoogle Scholar
Soda, G and Furnari, S (2012) Exploring the topology of the plausible: fs/QCA counterfactual analysis and the plausible fit of unobserved organizational configurations. Strategic Organization 10, 285–296.CrossRefGoogle Scholar
Stone, MH (1936) The theory of representations of Boolean algebras. Transactions of the American Mathematical Society, 40, 37–111.Google Scholar
Thiem, A (2019) Beyond the facts: limited empirical diversity and causal inference in QCA. Sociological Methods & Research. Online First. doi.org/10.1177/0049124119882463.CrossRefGoogle Scholar
Thomann, E and Maggetti, M (2020) Designing research with qualitative comparative analysis (QCA): approaches, challenges, and tools. Sociological Methods & Research 49, 356–86.CrossRefGoogle Scholar
Verba, S (1967) Some dilemmas in comparative research. World Politics 20, 111–127.CrossRefGoogle Scholar
Weingast, BR (1984) The congressional bureaucratic system: a principal-agent perspective. Public Choice 44, 147–191.CrossRefGoogle Scholar
Winship, C and Morgan, SL (1999) The estimation of causal effects from observational data. Annual Review of Sociology 25, 659–706.CrossRefGoogle Scholar
Supplementary material: Link

Damonte Dataset

Dataset

http://thedata.harvard.edu/dvn/dv/ipsr-risp
Link
Supplementary material: File

Damonte supplementary material

Damonte supplementary material 1

Download Damonte supplementary material(File)
File 1.1 KB
Supplementary material: PDF

Damonte supplementary material

Damonte supplementary material 2

Download Damonte supplementary material(PDF)
PDF 227.9 KB
Supplementary material: File

Damonte supplementary material

Damonte supplementary material 3

Download Damonte supplementary material(File)
File 1.5 KB