# Chapter 18 Glossary

term (typical) symbol meaning
Ambiguities matrix $$A$$ A matrix of 0s and 1s that maps from causal types (rows) to data types (columns). We call it an ambiguities matrix because the mapping from causal types to data types is many to one: Each causal type produces a unique data type, but a data type can be produced by many causal types.
Causal function $$f_Y(X, \theta_Y)$$ A function that maps from the possible values of the parents of a node to the possible values of the node. A change in the value of an argument is interpreted as a controlled change. Thus, $$f_Y(X=1, \theta_Y) - f_Y(X=0, \theta_Y)$$ can be interpreted as the change in $$Y$$ as $$X$$’s value is manipulated from 0 to 1. See Note 2.1.
Causal model M, M’ A triple containing: (1) a partially ordered set of (endogenous and exogenous) nodes, (2) a set of functions, one for each endogenous variable, specifying how it responds to the values of earlier variables in the ordering, (3) a probability distribution over exogenous variables. Note that (1) and (2) together define a “structural causal model” whereas (1), (2), and (3) describe a “probabilistic causal model” which we refer to simply as a causal model. See Definition 2.1.
Causal type $$\theta$$ A causal type is a concatenation of nodal types, one for each node. The causal type of a unit fully describes what values that unit takes on at all nodes and also how that unit would respond to all interventions. Example: $$(\theta^X_0, \theta^Y_{01})$$ is a causal type that has $$X=0$$ and $$Y=0$$ but would have $$Y=1$$ if $$X$$ were set to 1. Types like this are written in code in CausalQueries as X0.Y01.
Clue $$K$$ A variable or collection of variables whose values are potentially informative for some query.
Conditional independence $${\displaystyle (A\perp \!\!\!\perp B\mid C)}$$ Two (sets of) variables ($$A$$ and $$B$$) are conditionally independent given some third (set of) variables ($$C$$) if, for all $$a$$, $${\displaystyle \Pr(A=a\mid B,C)=\Pr(A=a\mid C)}$$ See Definition 2.2.
Credibility interval A set of possible values within which we believe a parameter lies with some specified probability. In tables we often use cred low and cred high to indicate the lower and upper bounds of a 95% credibility interval.
DAG Directed acyclic graph. A graphical representation of a structural causal model, indicating nodes, parent-child relations, and relations of conditional independence.
Data strategy $$S$$ A plan indicating for how many nodes data of different types will be gathered. A data strategy might indicate what new data will be gathered at one point as a function of what has already been seen at earlier points.
Dirichlet priors alpha, $$\alpha$$ Nonnegative numbers used to characterize a prior distribution over a simplex. The implied mean is the normalized vector $$\mu= \alpha/\sum_j\alpha_j$$ and the variance is $$\mu(1-\mu)/(1+\sum_j\alpha_j)$$. See Section 5.1.4.
Data type or event type A possible set of values on all nodes (including, possibly, NAs). Example: X0Y1 $$= (X=0, Y = 1)$$.
Endogenous node $$X$$, $$Y$$ A node that is a function of other nodes (whether these are just exogenous nodes, or a mix of endogenous and exogenous nodes). All substantive nodes in a model are typically endogenous in that they, minimally, have an exogenous ($$\theta^j$$) node pointing into them.
Event probability $$w$$ The probability of a data type or event type arising. Example: $$w_{01}=\Pr(X=0, Y=1)$$.
Exogenous node $$\theta^X$$, $$\theta^Y$$ A node that is not a function of other nodes in a model. Exogenous nodes are often not represented on causal graphs, but in general there is implicitly one exogenous node for each endogenous node. In this book’s use of causal models, exogenous nodes typically represent nodal types.
Flat priors We say priors are flat when they place equal weight on all possibilities. For instance, we refer to a Dirichlet as describing flat priors when $$\alpha$$ is a vector of 1s.
Mediator $$M$$ A mediator is a variable (node) that lies along the causal pathway of one variable to another and through which a causal effect may pass. For instance, in an $$X \rightarrow M \rightarrow Y$$ model, $$M$$ is a potential mediator for the effect of $$X$$ on $$Y$$.
Moderator $$K, M, W$$ A moderator is a variable that affects the effect of one variable on another. For instance, in an $$X \rightarrow Y \leftarrow K$$ model, $$K$$ is a potential moderator, potentially altering the affect of $$X$$ on $$Y$$
Multinomial distribution A probability distribution reporting the probability of a given distribution of outcomes across categories.
Nodal type $$\theta^X$$ The way that a node responds to the values of its parents. Example: $$\theta^Y_{10}$$, sometimes written Y10 is a nodal type for which $$Y$$ takes the value 1 if $$X=0$$ and 0 if $$X=1$$.
Parent (child) $$pa()$$ $$X$$ is a parent of $$Y$$ if a change in $$X$$ possibly induces a change in $$Y$$ even when all other nodes in the graphs are fixed. $$Y$$ is a child of $$X$$ if a change in $$X$$ sometimes induces a change in $$Y$$ even when all other nodes are fixed. On the graph, an arrow from $$X$$ to $$Y$$ indicates that $$X$$ is a parent of $$Y$$ and that $$Y$$ is a child of $$X$$.
Parameter $$\lambda$$ An unknown quantity of interest. In many applications in the book, $$\lambda^V_x$$ denotes the share of units that have nodal type $$x$$ on node $$V$$. In models with unobserved confounding, parameters are often thought of as the conditional probabilities of nodal types. Example: $$\lambda^Y_{01|\theta^M=\theta^M_{01}} = \Pr(\theta^Y = \theta^Y_{01}|\theta^M=\theta^M_{01})$$.
Parameter matrix $$P$$ A matrix of 0s and 1s that maps from parameters (rows) to causal types (columns).
Posterior $$p(\lambda|d)$$ A probability distribution over a set of parameter values after observing data.
Potential outcomes $$Y_i(0), Y_i(1)$$ The values that a unit would take on under a specified set of conditions—for instance, if $$X$$ were set to 0 or $$X$$ were set to 1. See Note 2.1.
Prior $$p(\lambda)$$ A probability distribution over a set of parameter values before observing data.
Query $$Q$$, $$q$$ A question asked of a model, either about the values of nodes or the values that nodes would take under specified operations. We use lower case $$q$$ to represent the answer to the query (the estimand), which is the realization of $$Q$$. Simple queries, such as the probability that $$X$$ has a positive effect on $$Y$$, ask about the probability of some set of causal types. Complex queries such as the average treatment effect, ask for summaries of simple queries: In a binary setup, the share of units with a positive effect less the share of units with a negative effect.
Acemoglu, Daron, Simon Johnson, and James A Robinson. 2001. “The Colonial Origins of Comparative Development: An Empirical Investigation.” American Economic Review 91 (5): 1369–1401.
Acemoglu, Daron, and James A Robinson. 2005. Economic Origins of Dictatorship and Democracy. New York: Cambridge University Press.
Angrist, Joshua D, and Guido W Imbens. 1995. “Identification and Estimation of Local Average Treatment Effects.” National Bureau of Economic Research.
Ansell, Ben W, and David J Samuels. 2014. Inequality and Democratization. New York: Cambridge University Press.
Aronow, Peter M, and Benjamin T Miller. 2019. Foundations of Agnostic Statistics. Cambridge University Press.
Bareinboim, Elias, and Judea Pearl. 2016. “Causal Inference and the Data-Fusion Problem.” Proceedings of the National Academy of Sciences 113 (27): 7345–52.
Bayarri, MJ, and James O Berger. 2000. “P Values for Composite Null Models.” Journal of the American Statistical Association 95 (452): 1127–42.
Beach, Derek, and Rasmus Brun Pedersen. 2013. Process-Tracing Methods: Foundations and Guidelines. Ann Arbor, MI: University of Michigan Press.
Bennett, Andrew. 2008. “Process Tracing. A Bayesian Perspective.” In The Oxford Handbook of Political Methodology, edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier, 702–21. Oxford, UK: Oxford University Press.
———. 2015. “Appendix.” In Process Tracing: From Metaphor to Analytic Tool, edited by Andrew Bennett and Jeffrey Checkel. New York: Cambridge University Press.
Bennett, Andrew, and Jeffrey Checkel. 2015a. “Process Tracing: From Philosophical Roots to Best Practices.” In Process Tracing: From Metaphor to Analytic Tool, edited by Andrew Bennett and Jeffrey Checkel, 3–37. New York: Cambridge University Press.
Bennett, Andrew, and Jeffrey T Checkel. 2015b. Process Tracing. New York: Cambridge University Press.
Blair, Graeme, Alexander Coppock, and Macartan Humphreys. 2023. Research Design: Declaration, Diagnosis, Redesign. Princeton University Press.
Boix, Carles. 2003. Democracy and Redistribution. New York: Cambridge University Press.
Bollen, Kenneth A, and Robert W Jackman. 1985. “Political Democracy and the Size Distribution of Income.” American Sociological Review 50 (4): 438–57.
Brady, H. E., and D. Collier. 2010. Rethinking Social Inquiriy: Diverse Tools, Shared Standards. Lanham, MD: Rowman & Littlefield. http://books.google.ca/books?id=djovjEXZYccC.
Cartwright, Nancy. 1989. “Nature’s Capacities and Their Measurement.”
———. 2007. Hunting Causes and Using Them: Approaches in Philosophy and Economics. Cambridge University Press.
Cheibub, José Antonio, Jennifer Gandhi, and James Raymond Vreeland. 2010. “Democracy and Dictatorship Revisited.” Public Choice 143 (1-2): 67–101.
Chickering, David M, and Judea Pearl. 1996. “A Clinician’s Tool for Analyzing Non-Compliance.” In Proceedings of the National Conference on Artificial Intelligence, 1269–76.
Clark, David, and Patrick Regan. 2016. “Mass Mobilization Protest Data. 2016.” Harvard Dataverse. https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HTTWYL.
Clarke, Kevin A, and David M Primo. 2012. A Model Discipline: Political Science and the Logic of Representations. New York: Oxford University Press.
Collier, David. 2011. “Understanding Process Tracing.” PS: Political Science & Politics 44 (04): 823–30.
Collier, David, Henry E Brady, and Jason Seawright. 2004. “Sources of Leverage in Causal Inference: Toward an Alternative View of Methodology.” In Rethinking Social Inquiry: Diverse Tools, Shared Standards, edited by David Collier and Henry E Brady, 229–66. Lanham, MD: Rowman & Littlefield.
———. 2010. “Sources of Leverage in Causal Inference: Toward an Alternative View of Methodology.” In Rethinking Social Inquiry: Diverse Tools, Shared Standards, edited by David Collier and Henry E Brady, 161–99. Lanham MD: Rowman; Littlefield.
Cook, Thomas D. 2018. “Twenty-Six Assumptions That Have to Be Met If Single Random Assignment Experiments Are to Warrant ‘Gold Standard’ Status: A Commentary on Deaton and Cartwright.” Social Science & Medicine.
Copas, JB. 1973. “Randomization Models for the Matched and Unmatched 2$$\times$$ 2 Tables.” Biometrika 60 (3): 467–76.
Coppock, Alexander, and Dipin Kaur. 2022. “Qualitative Imputation of Missing Potential Outcomes.” American Journal of Political Science.
Cowell, Robert G, Philip Dawid, Steffen L Lauritzen, and David J Spiegelhalter. 1999. Probabilistic Networks and Expert Systems. Springer.
Creswell, J. W., and Amanda L. Garrett. 2008. “The "Movement" of Mixed Methods Research and the Role of Educators.” South African Journal of Education 28: 321–33. http://www.scielo.org.za/scielo.php?pid=S0256-01002008000300003\&script=sci_arttext\&tlng=pt.
Dahl, Robert Alan. 1973. Polyarchy: Participation and Opposition. New Haven: Yale University Press.
Dawid, A Philip. 2010. “Beware of the DAG!” In Causality: Objectives and Assessment, 59–86. PMLR.
Dawid, Philip, Macartan Humphreys, and Monica Musio. 2019. “Bounding Causes of Effects with Mediators.” arXiv Preprint arXiv:1907.00399.
Druckman, James N, Donald P Green, James H Kuklinski, and Arthur Lupia. 2011. “Experimentation in Political Science.” In Handbook of Experimental Political Science, edited by James N Druckman, Donald P Green, James H Kuklinski, and Arthur Lupia, 3–14. New York: Cambridge University Press.
Dunning, T. 2012. Natural Experiments in the Social Sciences: A Design-Based Approach. Strategies for Social Inquiry. Cambridge University Press. http://books.google.de/books?id=ThxVBFZJp0UC.
Earman, John. 1992. Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. Cambridge, ma: MIT Press.
Fairfield, Tasha, and Andrew Charman. 2017. “Explicit Bayesian Analysis for Process Tracing: Guidelines, Opportunities, and Caveats.” Political Analysis 25 (3): 363–80.
Fairfield, Tasha, and Andrew E. Charman. forthcoming. Social Inquiry and Bayesian Inference: Rethinking Qualitative Research. Cambridge University Press.
Fearon, James, and David Laitin. 2008. “Integrating Qualitative and Quantitative Methods.” In Oxford Handbook of Political Methodology, edited by Janet M. Box-Steffenmeier, David Collier, and Henry E Brady, 756–76. Cambridge, UK: Oxford University Press.
Fisher, Ronald A. 2017. Design of Experiments. New York: Hafner.
Frangakis, Constantine E, and Donald B Rubin. 2002. “Principal Stratification in Causal Inference.” Biometrics 58 (1): 21–29.
Gabry, Jonah, Daniel Simpson, Aki Vehtari, Michael Betancourt, and Andrew Gelman. 2019. “Visualization in Bayesian Workflow.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 182 (2): 389–402.
Galbraith, James. 2016. “University of Texas Inequality Project.” https://utip.lbj.utexas.edu/default.html.
Galles, David, and Judea Pearl. 1998. “An Axiomatic Characterization of Causal Counterfactuals.” Foundations of Science 3 (1): 151–82.
García, Fernando Martel, and Leonard Wantchekon. 2015. “A Graphical Approximation to Generalization: Definitions and Diagrams.” Journal of Globalization and Development 6 (1): 71–86.
Gardner, Martin. 1961. The Second Scientific American Book of Mathematical Puzzles and Diversions. Simon; Schuster New York.
Gelman, Andrew. 2013. “Two Simple Examples for Understanding Posterior p-Values Whose Distributions Are Far from Uniform.” Electronic Journal of Statistics 7: 2595–2602.
Gelman, Andrew, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari, and Donald B Rubin. 2013. Bayesian Data Analysis. Boca Raton, FL: CRC Press.
George, Alexander L., and Andrew A. Bennett. 2005. Case Studies and Theory Development in the Social Sciences. A BCSIA Book. MIT Press. http://books.google.ch/books?id=JEGzE6ExN-gC.
George, Alexander L., and Timothy J McKeown. 1985. “Case Studies and Theories of Organizational Decision Making.” Advances in Information Processing in Organizations 2 (1): 21–58.
Gerber, Alan S., Donald P. Green, and Edward H. Kaplan. 2004. “The Illusion of Learning from Observational Research.” In Problems and Methods in the Study of Politics, edited by Ian Shapiro, Rogers M. Smith, and Tarek E. Masoud, 251–73. Cambridge, UK: Cambridge University Press.
Gerring, John. 2006. Case Study Research: Principles and Practices. New York: Cambridge University Press.
Geweke, John, and Gianni Amisano. 2014. “Analysis of Variance for Bayesian Inference.” Econometric Reviews 33 (1-4): 270–88.
Glymour, Clark, Kun Zhang, and Peter Spirtes. 2019. “Review of Causal Discovery Methods Based on Graphical Models.” Frontiers in Genetics 10: 524.
Glynn, Adam, and Kevin Quinn. 2007. “Non-Parametric Mechanisms and Causal Modeling.” working paper.
———. 2011. “Why Process Matters for Causal Inference.” Political Analysis 19: 273–86.
Goertz, G., and J. Mahoney. 2012. Tale of Two Cultures - Contrasting Qualitative and Quantitative. University Press Group Limited. http://books.google.de/books?id=3DZ6d0d2K3EC.
Good, Irving John. 1984. “C197. The Best Explicatum for Weight of Evidence.” Journal of Statistical Computation and Simulation 19 (4): 294–99.
Good, Isidore Jacob. 1950. “Probability and the Weighing of Evidence.”
Gordon, Sanford C, and Alastair Smith. 2004. “Quantitative Leverage Through Qualitative Knowledge: Augmenting the Statistical Analysis of Complex Causes.” Political Analysis 12 (3): 233–55.
Haggard, Stephan, and Robert R Kaufman. 2012. “Inequality and Regime Change: Democratic Transitions and the Stability of Democratic Rule.” American Political Science Review 106 (03): 495–516.
Haggard, Stephan, Robert R Kaufman, and Terence Teo. 2012. “Distributive Conflict and Regime Change: A Qualitative Dataset.” Coding Document to Accompany Haggard and Kaufman.
Hall, Ned. 2004. “Two Concepts of Causation.” Causation and Counterfactuals, 225–76.
Hall, Peter A. 2003. “Aligning Ontology and Methodology in Comparative Research.” In Comparative Historical Analysis in the Social Sciences, edited by James Mahoney and Dietrich Rueschemeyer, 373–404. Cambridge, UK; New York: Cambridge University Press; Cambridge University Press.
Halpern, Joseph Y. 2015. A modification of the Halpern-Pearl definition of causality.” arXiv Preprint arXiv:1505.00162.
———. 2016. Actual Causality. MIT Press.
Halpern, Joseph Y, and Judea Pearl. 2005. “Causes and Explanations: A Structural-Model Approach. Part i: Causes.” The British Journal for the Philosophy of Science 56 (4): 843–87.
Heckerman, David E, Eric J Horvitz, and Bharat N Nathwani. 1991. “Toward Normative Expert Systems: The Pathfinder Project.” Methods of Information in Medicine 31: 90I105.
Hernán, Miguel A, and James M Robins. 2006. “Instruments for Causal Inference: An Epidemiologist’s Dream?” Epidemiology 17 (4): 360–72.
Herron, Michael C, and Kevin M Quinn. 2016. “A Careful Look at Modern Case Selection Methods.” Sociological Methods & Research 45 (3): 458–92.
Hoffrage, Ulrich, and Gerd Gigerenzer. 1998. “Using Natural Frequencies to Improve Diagnostic Inferences.” Academic Medicine 73 (5): 538–40.
Holland, Paul W. 1986. “Statistics and Causal Inference.” Journal of the American Statistical Association 81 (396): 945–60.
Hume, David, and Tom L Beauchamp. 2000. An Enquiry Concerning Human Understanding: A Critical Edition. Vol. 3. Oxford University Press.
Humphreys, Macartan, and Alan M Jacobs. 2015. “Mixing Methods: A Bayesian Approach.” American Political Science Review 109 (04): 653–73.
Humphreys, Macartan, and Jeremy M Weinstein. 2009. “Field Experiments and the Political Economy of Development.” Annual Review of Political Science 12: 367–78.
Huntington, Samuel P. 1993. The Third Wave: Democratization in the Late Twentieth Century. Norman, OK: University of Oklahoma Press.
Imai, Kosuke, Luke Keele, and Dustin Tingley. 2010. “A General Approach to Causal Mediation Analysis.” Psychological Methods 15 (4): 309.
Jeffreys, Harold. 1998. The Theory of Probability. OUP Oxford.
Kaye, David H. 1986. “Quantifying Probative Value.” BUL Rev. 66: 761.
Kaye, David H, and Jonathan J Koehler. 2003. “The Misquantification of Probative Value.” Law and Human Behavior 27 (6): 645–59.
King, Gary. 1998. Unifying Political Methodology: The Likelihood Theory of Statistical Inference. University of Michigan Press.
King, Gary, Robert Keohane, and Sidney Verba. 1994. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton University Press. http://books.google.de/books?id=A7VFF-JR3b8C.
Knox, Dean, Will Lowe, and Jonathan Mummolo. 2020. “Administrative Records Mask Racially Biased Policing.” American Political Science Review 114 (3): 619–37.
Knox, Dean, Teppei Yamamoto, Matthew A Baum, and Adam J Berinsky. 2019. “Design, Identification, and Sensitivity Analysis for Patient Preference Trials.” Journal of the American Statistical Association, 1–27.
Laplace, Pierre-Simon. 1901. A Philosophical Essay on Probabilities. Translated by F.W. Truscott and F.L. Emory. Vol. 166. New York: Cosimo.
Lewis, David. 1973. “Counterfactuals and Comparative Possibility.” In Ifs, 57–85. Springer.
———. 1986. “Causation.” Philosophical Papers 2: 159–213.
Lieberman, Evan S. 2010. “Bridging the Qualitative-Quantitative Divide: Best Practices in the Development of Historically Oriented Replication Databases.” Annual Review of Political Science 13: 37–59.
Lieberman, Evan S. 2003. Race and Regionalism in the Politics of Taxation in Brazil and South Africa. Cambridge Studies in Comparative Politics. Cambridge University Press. http://books.google.de/books?id=S6BOgyL-KYQC.
———. 2005. “Nested Analysis as a Mixed-Method Strategy for Comparative Research.” American Political Science Review 99 (July): 435–52. https://doi.org/10.1017/S0003055405051762.
Lindley, Dennis V. 1956. “On a Measure of the Information Provided by an Experiment.” The Annals of Mathematical Statistics, 986–1005.
Linz, Juan J, and Alfred Stepan. 1996. Problems of Democratic Transition and Consolidation: Southern Europe, South America, and Post-Communist Europe. Baltimore: Johns Hopkins University Press.
Maclaren, Oliver J, and Ruanui Nicholson. 2019. “What Can Be Estimated? Identifiability, Estimability, Causal Inference and Ill-Posed Inverse Problems.” arXiv Preprint arXiv:1904.02826.
Mahoney, James. 2000. “Strategies of Causal Inference in Small-n Analysis.” Sociological Methods & Research 28 (4): 387–424.
———. 2008. “Toward a Unified Theory of Causality.” Comparative Political Studies 41 (4-5): 412–36.
———. 2010. “After KKV: The New Methodology of Qualitative Research.” World Politics 62 (01): 120–47.
———. 2012. “The Logic of Process Tracing Tests in the Social Sciences.” Sociological Methods and Research 41 (4): 570–97. http://EconPapers.repec.org/RePEc:sae:somere:v:41:y:2012:i:4:p:570-597.
Manski, Charles F. 1995. Identification Problems in the Social Sciences. Harvard University Press.
Meltzer, Allan H, and Scott F Richard. 1981. “A Rational Theory of the Size of Government.” Journal of Political Economy 89 (5): 914–27.
Menzies, Peter. 1989. “Probabilistic Causation and Causal Processes: A Critique of Lewis.” Philosophy of Science, 642–63.
Méon, Pierre-Guillaume, and Khalid Sekkat. 2005. “Does Corruption Grease or Sand the Wheels of Growth?” Public Choice 122 (1): 69–97.
Mosley, Layna. 2013. Interview Research in Political Science. Cornell University Press.
Palfrey, Thomas R. 2009. “Laboratory Experiments in Political Economy.” Annual Review of Political Science 12: 379–88.
Parsons, Simon. 2001. Qualitative Methods for Reasoning Under Uncertainty. Vol. 13. Mit Press.
Pearl, Judea. 2000. Causality: Models, Reasoning and Inference. Vol. 29. Cambridge Univ Press.
———. 2009. Causality. Cambridge university press.
———. 2010. “An Introduction to Causal Inference.” The International Journal of Biostatistics 6 (2): 1–62.
———. 2012. “The Causal Foundations of Structural Equation Modeling.” DTIC Document.
Pearl, Judea, and Elias Bareinboim. 2014. “External Validity: From Do-Calculus to Transportability Across Populations.” Statistical Science 29 (4): 579–95.
Peressini, Anthony. 1999. “Applying Pure Mathematics.” Philosophy of Science 66: S1–13.
Pierson, Paul. 1994. Dismantling the Welfare State?: Reagan, Thatcher and the Politics of Retrenchment. Cambridge University Press.
Przeworski, Adam, and Fernando Limongi. 1997. “Modernization: Theories and Facts.” World Politics 49 (2): 155–83.
Raiffa, Howard, and Robert Schlaifer. 1961. “Applied Statistical Decision Theory.”
Rodrik, Dani, Arvind Subramanian, and Francesco Trebbi. 2004. “Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development.” Journal of Economic Growth 9 (2): 131–65.
Rohlfing, I. 2012. Case Studies and Causal Inference: An Integrative Framework. Research Methods Series. New York: Palgrave Macmillan. http://books.google.ca/books?id=4W\_XuA3njRQC.
Rohlfing, Ingo. 2013. “Comparative Hypothesis Testing via Process Tracing.” Sociological Methods & Research 43 (04): 606–42.
Rohrer, Julia M. 2018. “Thinking Clearly about Correlations and Causation: Graphical Causal Models for Observational Data.” Advances in Methods and Practices in Psychological Science 1 (1): 27–42.
Rubin, Donald B. 1974. “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies.” Journal of Educational Psychology 66: 688–701.
Sachs, Jeffrey D. 2001. “Tropical Underdevelopment.”
Saunders, Elizabeth N. 2011. Leaders at War: How Presidents Shape Military Interventions. Cornell University Press.
Scharf, Louis L. 1991. Statistical Signal Processing. Vol. 98. Addison-Wesley Reading, MA.
Seawright, Jason. 2016. Multi-Method Social Science: Combining Qualitative and Quantitative Tools. New York: Cambridge University Press.
Seawright, Jason, and John Gerring. 2008. “Case Selection Techniques in Case Study Research: A Menu of Qualitative and Quantitative Options.” Political Research Quarterly 61 (2): 294–308. https://doi.org/10.1177/1065912907313077.
Small, Mario Luis. 2011. “How to Conduct a Mixed Methods Study: Recent Trends in a Rapidly Growing Literature.” Annual Review of Sociology 37: 57–86.
Spirtes, Peter, Clark N Glymour, Richard Scheines, and David Heckerman. 2000. Causation, Prediction, and Search. MIT press.
Splawa-Neyman, Jerzy, DM Dabrowska, TP Speed, et al. 1990. “On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.” Statistical Science 5 (4): 465–72.
Stan, Development Team et al. 2020. “Stan Modeling Language Users Guide and Reference Manual.” Technical Report. https://mc-stan.org/docs/2_24/reference-manual/index.html.
Stokes, S. C. 2001. Mandates and Democracy: Neoliberalism by Surprise in Latin America. Cambridge Studies in Comparative Politics. Cambridge University Press. http://books.google.de/books?id=-cdcSVFZRU8C.
Swank, D. 2002. Global Capital, Political Institutions, and Policy Change in Developed Welfare States. Cambridge Studies in Comparative Politics. Cambridge University Press. http://books.google.de/books?id=p3F-agj4CXcC.
Tamer, Elie. 2010. “Partial Identification in Econometrics.” Annu. Rev. Econ. 2 (1): 167–95.
Thelen, Kathleen, and James Mahoney. 2015. “Comparative-Historical Analysis in Contemporary Political Science.” In Advances in Comparative-Historical Analysis, 1–36. New York: Cambridge University Press.
Van Evera, Stephen. 1997. Guide to Methods for Students of Political Science. Ithaca, NY: Cornell University Press.
Waldner, David. 2015. “What Makes Process Tracing Good? Causal Mechanisms, Causal Inference, and the Completeness Standard in Comparative Politics.” In Process Tracing: From Metaphor to Analytic Tool, edited by Andrew Bennett and Jeffrey Checkel, 126–52. New York: Cambridge University Press.
Weller, Nicholas, and Jeb Barnes. 2014. Finding Pathways: Mixed-Method Research for Studying Causal Mechanisms. New York: Cambridge University Press.
Western, Bruce, and Simon Jackman. 1994. “Bayesian Inference for Comparative Research.” American Political Science Review 88 (02): 412–23.
Woodward, James. 2003. “Scientific Explanation.” The Stanford.