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  3. Causal Factor Investing
  • Cited by 12
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    • Publisher:
      Cambridge University Press
      Publication date:
      12 October 2023
      09 November 2023
      ISBN:
      9781009397315
      9781009702416
      9781009397292
      DOI:
      https://doi.org/10.1017/9781009397315
      Creative Commons:
      Creative Common License - CC Creative Common License - BY Creative Common License - NC
      This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY-NC 4.0.
      https://creativecommons.org/creativelicenses
      Dimensions:
      Weight & Pages:
      0.271kg, 92 Pages
      Dimensions:
      (229 x 152 mm)
      Weight & Pages:
      0.16kg, 92 Pages
      • Subjects:
      Economics, Finance and Accountancy
      Series:
      Elements in Quantitative Finance
    Open Access
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    Subjects:
    Economics, Finance and Accountancy
    Series:
    Elements in Quantitative Finance
    Information

    Book description

    Virtually all journal articles in the factor investing literature make associational claims, in denial of the causal content of factor models. Authors do not identify the causal graph consistent with the observed phenomenon, they justify their chosen model specification in terms of correlations, and they do not propose experiments for falsifying causal mechanisms. Absent a causal theory, their findings are likely false, due to rampant backtest overfitting and incorrect specification choices. This Element differentiates between type-A and type-B spurious claims, and explains how both types prevent factor investing from advancing beyond its current phenomenological stage. It analyzes the current state of causal confusion in the factor investing literature, and proposes solutions with the potential to transform factor investing into a truly scientific discipline. This title is also available as Open Access on Cambridge Core.

    References

    References

    Abadie, A. (2021): “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects.” Journal of Economic Literature, Vol. 59, No. 2, pp. 391–425.
    Abadie, A. and Cattaneo, M. (2018): “Econometric Methods for Program Evaluation.” Annual Review of Economics, Vol. 10, pp. 465–503.
    Anderson, R. (2011): “Time-Varying Risk Premia.” Journal of Mathematical Economics, Vol. 47, No. 3, pp. 253–259.
    Angrist, J. and Pischke, J. (2008): Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press, 1st ed.
    Angrist, J. and Pischke, J. (2010): “The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics.” National Bureau of Economic Research, Working Paper 15794. www.nber.org/papers/w15794.
    Ashenfelter, O. and Card, D. (1986): “Why Have Unemployment Rates in Canada and the United States Diverged?” Economica, Vol. 53, No. 210, pp. S171–S195.
    Athey, S. and Imbens, G. (2019): “Machine Learning Methods That Economists Should Know about.” Annual Review of Economics, Vol. 11, pp. 685–725.
    Baba, K., Shibata, R., and Sibuya, M. (2004): “Partial Correlation and Conditional Correlation as Measures of Conditional Independence.” Australian and New Zealand Journal of Statistics, Vol. 46, No. 4, pp. 657–664.
    Bailey, D., Borwein, J., López de Prado, M., and Zhu, J. (2014): “Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance.” Notices of the American Mathematical Society, Vol. 61, No. 5, pp. 458–471.
    Bailey, D. and López de Prado, M. (2012): “The Sharpe Ratio Efficient Frontier.” Journal of Risk, Vol. 15, No. 2, pp. 3–44.
    Bailey, D. and López de Prado, M. (2014): “The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality.” Journal of Portfolio Management, Vol. 40, No. 5, pp. 94–107.
    Bailey, D. and López de Prado, M. (2021): “How ‘Backtest Overfitting’ in Finance Leads to False Discoveries.” Significance (Royal Statistical Society), Vol. 18, No. 6, pp. 22–25.
    Baker, M. and Wurgler, J. (2007): “Investor Sentiment in the Stock Market.” Journal of Economic Perspectives, Vol. 21, No. 2, pp. 129–152.
    Balsubramani, A. and Ramdas, A. (2016): “Sequential Nonparametric Testing with the Law of the Iterated Logarithm.” ArXiv, Working Paper. https://arxiv.org/pdf/1506.03486.pdf.
    Barroso, P. and Santa-Clara, P. (2015): “Momentum Has Its Moments.” Journal of Financial Economics, Vol. 116, No. 1, pp. 111–120.
    Benjamini, Y. and Hochberg, Y. (1995): “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society, Series B, Vol. 57, No. 1, pp. 125–133.
    Berk, J., Green, R., and Naik, V. (1999): “Optimal Investment, Growth Options, and Security Returns.” Journal of Finance, Vol. 54, pp. 1153–1607.
    Berkson, J. (1946): “Limitations of the Application of Fourfold Table Analysis to Hospital Data.” Biometrics Bulletin, Vol. 2, No. 3, pp. 47–53.
    BlackRock (2022): “What Is Factor Investing?” www.blackrock.com/us/individual/investment-ideas/what-is-factor-investing.
    Bloomberg (2021): “Bloomberg GSAM US Equity Multi Factor Index.” Bloomberg Professional Services – Indices. Available through the Bloomberg Terminal. https://assets.bbhub.io/professional/sites/10/Bloomberg-GSAM-US-Equity-Multi-Factor-Index-Fact-Sheet.pdf
    Bronzoni, R. and Iachini, E. (2014): “Are Incentives for R&D Effective? Evidence from a Regression Discontinuity Approach.” American Economic Journal: Economic Policy, Vol. 6, No. 4, pp. 100–134.
    Carhart, M. (1997): “On Persistence in Mutual Fund Performance.” Journal of Finance, Vol. 52, No. 1, pp. 57–82.
    Chatfield, C. (1995): “Model Uncertainty, Data Mining and Statistical Inference.” Journal of the Royal Statistical Society. Series A, Vol. 158, No. 3, pp. 419–466.
    Chen, B. and Pearl, J. (2013): “Regression and Causation: A Critical Examination of Six Econometrics Textbooks.” Real-World Economics Review, No. 65, pp. 2–20. http://www.paecon.net/PAEReview/issue65/ChenPearl65.pdf.
    Chickering, D. (2003): “Optimal Structure Identification with Greedy Search.” Journal of Machine Learning Research, Vol. 3, pp. 507–554.
    Cochrane, J. (2005): Asset Pricing. Princeton University Press, 1st ed.
    Cochrane, J. (2011): “Presidential Address: Discount Rates.” Journal of Finance, Vol. 66, No. 4, pp. 1047–1108.
    Cong, L., Tang, K., Wang, J., and Zhang, Y. (2021): “AlphaPortfolio: Direct Construction through Deep Reinforcement Learning and Interpretable AI.” SSRN, Working Paper. https://ssrn.com/abstract=3554486.
    Das, S. and Chen, M. (2007): “Yahoo! For Amazon: Sentiment Extraction from Small Talk on the Web.” Management Science, Vol. 53, No. 9, pp. 1375–1388.
    De Miguel, V., Garlappi, L., and Uppal, R. (2009): “Optimal versus Naive Diversification: How Inefficient Is the 1/N Portfolio Strategy?” Review of Financial Studies, Vol. 22, No. 5, pp. 1915–1953.
    Denev, A. (2015): Probabilistic Graphical Models: A New Way of Thinking in Financial Modelling. Risk Books, 1st ed.
    Dickson, M. and Baird, D. (2011): “Significance Testing.” In Philosophy of Statistics, pp. 199–232, edited by Bandyopadhyay, P. and Forster, M.. Elsevier, 1st ed.
    Diebold, F. (2007): Elements of Forecasting. Thomson South-Western, 4th ed.
    Dryden, J. (1697): The Works of Virgil Containing His Pastorals, Georgics and Aeneis. https://quod.lib.umich.edu/e/eebo/A65112.0001.001/1:18.2?rgn=div2;view=fulltext.
    Dunning, T. (2012): Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press, 1st ed.
    Easley, D., Kiefer, N., O’Hara, M., and Paperman, J. (1996): “Liquidity, Information, and Infrequently Traded Stocks.” Journal of Finance, Vol. 51, No. 4, pp. 1405–1436.
    Easley, D., Prado, M. López de, and O’Hara, M. (2010): “Measuring Flow Toxicity in a High-Frequency World.” SSRN Working Paper. http://ssrn.com/abstract=1695596.
    Easley, D., Prado, M. López de, and O’Hara, M. (2012): “Flow Toxicity and Liquidity in a High-Frequency World.” Review of Financial Studies, Vol. 25, No. 5, pp. 1457–1493.
    Easley, D., de Prado, M. López, and O’Hara, M. (2016): “Discerning Information from Trade Data.” Journal of Financial Economics, Vol. 120, No. 2, pp. 269–285.
    Eaton, M. (1983): Multivariate Statistics: A Vector Space Approach. Wiley, 1st ed.
    Efron, B. and Hastie, T. (2021): Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press, 1st ed. https://hastie.su.domains/CASI_files/PDF/casi.pdf.
    Evans, M. (1994): “Expected Returns, Time-Varying Risk, and Risk Premia.” Journal of Finance, Vol. 49, No. 2, pp. 655–679.
    Fabozzi, F. and Prado, M. López de (2018): “Being Honest in Backtest Reporting: A Template for Disclosing Multiple Tests.” Journal of Portfolio Management, Vol. 45, No. 1, pp. 141–147.
    Fama, E. (1970): “Efficient Capital Markets: A Review of Theory and Empirical Work.” Journal of Finance, Vol. 25, No. 2, pp. 383–417.
    Fama, E. and French, K. (1993): “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics, Vol. 33, No. 1, pp. 3–56.
    Fama, E. and French, K. (1996): “Multifactor Explanations of Asset Pricing Anomalies.” Journal of Finance, Vol. 51, No. 1, pp. 55–84.
    Fama, E. and French, K. (2015): “A Five-Factor Asset Pricing Model.” Journal of Financial Economics, Vol. 116, No. 1, pp. 1–22.
    Fama, E. and MacBeth, J. (1973): “Risk Return and Equilibrium: Empirical Tests.” Journal of Political Economy, Vol. 71, pp. 607–636.
    Ferson, W. (2019): Empirical Asset Pricing: Models and Methods. MIT Press, 1st ed.
    Fisher, R. (1971): The Design of Experiments. Macmillan, 9th ed.
    Flammer, C. (2015): “Corporate Social Responsibility and the Allocation of Procurement Contracts: Evidence from a Natural Experiment.” Working Paper, Boston University. https://corporate-sustainability.org/wp-content/uploads/Corporate-Social-Responsibility.pdf.
    Gelman, A. and Rohilla-Shalizi, C. (2013): “Philosophy and the Practice of Bayesian Statistics.” British Journal of Mathematical and Statistical Psychology, Vol. 66, pp. 8–38.
    Gensler, H. (2010): Introduction to Logic. Routledge, 2nd ed.
    Glymour, C., Zhang, K., and Spirtes, P. (2019): “Review of Causal Discovery Methods Based on Graphical Models.” Frontiers in Genetics, Vol. 10, p. 524. pp. 1–15, www.frontiersin.org/articles/10.3389/fgene.2019.00524/full.
    Gomes, J., Kogan, L., and Zhang, L. (2003): “Equilibrium Cross Section of Returns.” Journal of Political Economy, Vol. 111, pp. 693–732.
    Granger, C. (1969): “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods.” Econometrica, Vol. 37, No. 3, pp. 424–438.
    Granger, C. and Newbold, P. (1974): “Spurious Regressions in Econometrics.” Journal of Econometrics, Vol. 2, No. 2, pp. 111–120.
    Granger, C. (1980): “Testing for Causality: A Personal Viewpoint.” Journal of Economic Dynamics and Control, Vol. 2, pp. 329–352.
    Granger, C., Hyung, N., and Jeon, Y. (2001): “Spurious Regressions with Stationary Series.” Applied Economics, Vol. 33, No. 7, pp. 899–904.
    Greene, W. (2012): Econometric Analysis. Pearson Education, 7th ed.
    Haavelmo, T. (1944): “The probability approach in econometrics.” Econometrica, Vol. 12, Supplement (July, 1944), pp. 1–115.
    Hamilton, J. (1994): Time Series Analysis. Princeton, 1st ed.
    Harvey, C., Liu, Y., and Zhu, H. (2016): “… and the Cross-Section of Expected Returns.” Review of Financial Studies, Vol. 29, No. 1, pp. 5–68.
    Harvey, C. (2017): “Presidential Address: The Scientific Outlook in Financial Economics.” Journal of Finance, Vol. 72, No. 4, pp. 1399–1440.
    Harvey, C. (2022): “The Pitfalls of Asset Management Research.” Journal of Systematic Investment, Vol. 2, No. 1, pp. 1–9. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4078138.
    Hassani, H., Huang, X., and Ghodsi, M. (2018): “Big Data and Causality.” Annals of Data Science, Vol. 5, pp. 133–156.
    Heilbron, J. (ed.) (2003): “Preface.” In The Oxford Companion to the History of Modern Science. Oxford University Press, pp. vii–x.
    Hernán, M. and Robins, J. (2020): Causal Inference: What If. CRC Press, 1st ed.
    Hill, R., Griffiths, W., and Lim, G. (2011): Principles of Econometrics. John Wiley, 4th ed.
    Hochberg, Y. (1988): “A Sharper Bonferroni Procedure for Multiple Tests of Significance.” Biometrika, Vol. 75, pp. 800–802.
    Hoyer, P., Janzing, D., Mooji, J., Peters, J., and Schölkopf, B. (2009): “Nonlinear Causal Discovery with Additive Noise Models.” In Advances in Neural Information Processing Systems, edited by Michael I. Jordan, Yann LeCun, and Sara A. Solla. Vol. 21. https://mitpress.mit.edu/9780262561457/advances-in-neural-information-processing-systems/.
    Imai, K. (2013): “Statistical Hypothesis Tests.” Course Materials, Department of Politics, Princeton University. https://imai.fas.harvard.edu/teaching/files/tests.pdf.
    Imbens, G. and Lemieux, T. (2008): “Regression Discontinuity Designs: A Guide to Practice.” Journal of Econometrics, Vol. 142, No. 2, pp. 615–635.
    Imbens, G. and Wooldridge, J. (2009): “Recent Developments in the Econometrics of Program Evaluation.” Journal of Econometric Literature, Vol. 47, pp. 5–86.
    Isichenko, M. (2021): Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage. Wiley, 1st ed.
    Johnson, T. (2002): “Rational Momentum Effects.” Journal of Finance, Vol. 57, pp. 585–608.
    Jones, B. and Kenward, M. (2003): Design and Analysis of Cross-Over Trials. London: Chapman & Hall/CRC, 2nd ed.
    Kalnins, A. (2022): “When Does Multicollinearity Bias Coefficients and Cause Type 1 Errors? A Reconciliation of Lindner, Puck, and Verbeke (2020) with Kalnins (2018).” Journal of International Business Studies, Vol. 53, pp. 1536–1548.
    Katayama, D. and Tsuda, K. (2020): “A Method of Using News Sentiment for Stock Investment Strategy.” Procedia Computer Science, Vol. 176, pp. 1971–1980.
    Kennedy, P. (2008): A Guide to Econometrics. MIT Press, 6th ed.
    Kohavi, R., Tang, D., Xu, Y., Hemkens, L., and Ioannidis, J. (2020): “Online Randomized Controlled Experiments at Scale: Lessons and Extensions to Medicine.” Trials, Vol. 21 Article 150, pp. 1–9. https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-020-4084-y.
    Koller, D. and Friedman, N. (2009): Probabilistic Graphical Models: Principles and Techniques. Adaptive Computation and Machine Learning. MIT Press, 1st ed.
    Lakatos, I. (1978): The Methodology of Scientific Research Programmes. Philosophical Papers, Vol. 1. Cambridge University Press, 1st ed.
    Laudy, O., Denev, A., and Ginsberg, A. (2022): “Building Probabilistic Causal Models Using Collective Intelligence.” The Journal of Financial Data Science, Vol. 4, No. 2, pp. 83–109.
    Lazear, E. (2000): “Economic Imperialism.” The Quarterly Journal of Economics, Vol. 115, No. 1, pp. 99–146.
    Leamer, E. (1983): “Let’s Take the Con Out of Econometrics.” American Economic Review, Vol. 73, No. 1, pp. 31–43.
    Li, D. and Zhang, L. (2010): “Does q-Theory with Investment Frictions Explain Anomalies in the Cross-Section of Returns?” Journal of Financial Economics, Vol. 98, pp. 297–314.
    Li, E., Livdan, D., and Zhang, L. (2009): “Anomalies.” Review of Financial Studies, Vol. 22, pp. 4301–4334.
    Lipton, A. (2016): “Macroeconomic Theories: Not Even Wrong.” Risk, August 22. www.risk.net/cutting-edge/views/2468446/macroeconomic-theories-not-even-wrong.
    Liu, L., Whited, T., and Zhang, L. (2009): “Investment-Based Expected Stock Returns.” Journal of Political Economy, Vol. 117, pp. 1105–1139.
    Liu, L. and Zhang, L. (2008): “Momentum Profits, Factor Pricing, and Macroeconomic Risk.” Review of Financial Studies, Vol. 21, pp. 2417–2448.
    López, de Prado, M. (2017): “Finance as an Industrial Science.” Journal of Portfolio Management, Vol. 43, No. 4, pp. 5–9.
    López de Prado, M. (2018): Advances in Financial Machine Learning. Wiley, 1st ed.
    López de Prado, M. (2019): “A Data Science Solution to the Multiple-Testing Crisis in Financial Research.” Journal of Financial Data Science, Vol. 1, No. 1, pp. 99–110.
    López de Prado, M. (2020): Machine Learning for Asset Managers. Cambridge University Press, 1st ed.
    López de Prado, M. (2022a): “Machine Learning for Econometricians: A ReadMe Manual.” Journal of Financial Data Science, Vol. 4, No. 3, pp. 1–21.
    López de Prado, M. (2022b): “Type I and Type II Errors of the Sharpe Ratio under Multiple Testing.” Journal of Portfolio Management, Vol. 49, No. 1, pp. 39–46.
    López de Prado, M. and Bailey, D. (2020): “The False Strategy Theorem: A Financial Application of Experimental Mathematics.” American Mathematical Monthly, Vol. 128, No. 9, pp. 825–831.
    López de Prado, M. and Lewis, M. (2019): “Detection of False Investment Strategies Using Unsupervised Learning Methods.” Quantitative Finance, Vol. 19, No. 9, pp. 1555–1565.
    Malenko, N. and Shen, Y. (2016): “The Role of Proxy Advisory Firms: Evidence from a Regression-Discontinuity Design.” Review of Financial Studies, Vol. 29, No. 12, pp. 3394–3427.
    Mullainathan, S. and Spiess, J. (2017): “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives, Vol. 31, No. 2, pp. 87–106.
    Neal, B. (2020): Introduction to Causal Inference: From a Machine Learning Perspective. Course Lecture Notes (December 17, 2020). www.bradyneal.com/causal-inference-course.
    Berman, Neuberger (2019): “Inside the Quant Investing Trend.” Quarterly Views. www.nb.com/documents/public/en-us/Messinger_Client_Letter_2Q19.pdf.
    Pearl, J. (1995): “Causal Diagrams for Empirical Research.” Biometrika, Vol. 82, pp. 669–710.
    Pearl, J. (2009): Causality: Models, Reasoning and Inference. Cambridge, 2nd ed.
    Pearl, J. (2015): “Trygve Haavelmo and the Emergence of Causal Calculus.” Econometric Theory, Vol. 31, pp. 152–179.
    Pearl, J., Glymour, M., and Jewell, N. (2016): Causal Inference in Statistics: A Primer. Wiley, 1st ed.
    Pearl, J. and MacKenzie, D. (2018): The Book of Why. Basic Books, 1st ed.
    Peierls, R. (1992): “Where Pauli Made His ‘Wrong’ Remark.” Physics Today, Vol. 45, No. 12, p. 112.
    Perezgonzalez, J. (2017): “Commentary: The Need for Bayesian Hypothesis Testing in Psychological Science.” Frontiers of Psychology, Vol. 8, Article 1434 pp. 1–3.
    Peters, L., Janzing, D., and Scholkopf, B. (2017): Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press, 1st ed.
    Pogosian, L., Raveri, M., Koyama et al., K. (2022): “Imprints of Cosmological Tensions in Reconstructed Gravity.” Nature Astronomy, Vol. 6, pp. 1484–1490. Forthcoming. https://doi.org/10.1038/s41550-022-01808-7.
    Popper, K. (1963): Conjectures and Refutations. Routledge and Kegan Paul, 1st ed.
    Popper, K. (1994a): “Falsifizierbarkeit, zwei Bedeutungen von.” In Handlexikon zur Wissenschaftstheorie, pp. 82–86, edited by Seiffert, H. and Radnitzky, G.. Ehrenwirth GmbH Verlag, 2nd ed.
    Popper, K. (1994b): The Myth of the Framework. In Defense of Science and Rationality. Routledge, 1st ed.
    Rad, H., Low, R., and Faff, R. (2016): “The Profitability of Pairs Trading Strategies: Distance, Cointegration and Copula Methods.” Quantitative Finance, Vol. 16, No. 10, pp. 1541–1558.
    Rao, C. (2000): Understanding Chemistry. Universities Press, 1st ed.
    Rebonato, R. (2010): Coherent Stress Testing. Wiley, 1st ed.
    Rebonato, R. and Denev, A. (2014): Portfolio Management under Stress: A Bayesian-Net Approach to Coherent Asset Allocation. Cambridge University Press, 1st ed.
    Reeves, C. and Brewer, J. (1980): “Hypothesis Testing and Proof By Contradiction: An Analogy.” Teaching Statistics, Vol. 2, No. 2, pp. 57–59.
    Robins, J. (1986): “A New Approach to Causal Inference in Mortality Studies with a Sustained Exposure Period: Application to Control of a Healthy Worker Survivor Effect.” Mathematical Modelling, Vol. 7, No. 9–12, pp. 1393–1512.
    Romano, J. and Wolf, M. (2005): “Stepwise Multiple Testing as Formalized Data Snooping.” Econometrica, Vol. 73, No. 4, pp. 1237–1282.
    Ruud, P. (2000): An Introduction to Classical Econometric Theory. Oxford University Press.
    Sabra, A. (1989): The Optics of Ibn al-Haytham. Books I-II-III: On Direct Vision. The Warburg Institute, University of London, 1st ed.
    Sagi, J. and Seasholes, M. (2007): “Firm-Specific Attributes and the Cross-Section of Momentum.” Journal of Financial Economics, Vol. 84, pp. 389–434.
    Schipper, K. and Thompson, R. (1981): “Common Stocks as Hedges against Shifts in the Consumption or Investment Opportunity Set.” Journal of Business, Vol. 1, pp. 305–328.
    Schuller, M., Haberl, A., and Zaichenkov, I. (2021): “Causality Testing in Equity Markets.” Working Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3941647.
    Shimizu, S., Hoyer, P., Hyvärinen, A., and Kerminen, A. (2006): “A Linear Non-Gaussian Acyclic Model for Causal Discovery.” Journal of Machine Learning Research, Vol. 7, pp. 2003–2030.
    Shpitser, I. and Pearl, J. (2006): “Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models.” Proceedings of the Twenty-First National Conference on Artificial Intelligence. AAAI Press, pp. 1219–1226.
    Spirtes, P., Glymour, C., and Scheines, R. (2000): “Constructing Bayesian Networks Models of Gene Expression Networks from Microarray Data.” Proceedings of the Atlantic Symposium on Computational Biology (North Carolina). https://kilthub.cmu.edu/articles/journal_contribution/Constructing_Bayesian_Network_Models_of_Gene_Expression_Networks_from_Microarray_Data/6491291.
    Spirtes, P., Glymour, C., and Scheines, R. (2001): Causation, Prediction, and Search. MIT Press, 2nd ed.
    Thiele, R. (2005): “In Memoriam: Matthias Schramm, 1928–2005.” Historia Mathematica, Vol. 32, pp. 271–274.
    Toomer, G. (1964): “Review: Ibn al-Haythams Weg zur Physik by Matthias Schramm.” Isis, Vol. 55, No. 4, pp. 463–465.
    Vignero, L. and Wenmackers, S. (2021): “Degrees of Riskiness, Falsifiability, and Truthlikeness.” Synthese, Vol. 199, pp. 11729–11764. https://doi.org/10.1007/s11229-021-03310-5.
    Vlastos, G. (1983): “The Socratic Elenchus.” Oxford Studies in Ancient Philosophy. Oxford University Press, 1st ed.
    Wasserstein, R. and Lazar, N. (2016): “The ASA Statement on p-Values: Context, Process, and Purpose.” The American Statistician, Vol. 70, No. 2, pp. 129–133.
    Webster, K. and Westray, N. (2022): “Getting More for Less: Better A/B Testing via Causal Regularization.” Working Paper. https://ssrn.com/abstract_id=4160945.
    White, H. (2000): “A Reality Check for Data Snooping.” Econometrica, Vol. 68, No. 5, pp. 1097–1126.
    Wieten, R., Bex, F., Prakken, H., and Renooij, S. (2020): “Deductive and Abductive Reasoning with Causal and Evidential Information.” In Computational Modes of Arguments, pp. 383–394, edited by Prakken, H., Bistarelli, S., Santini, F., and Taticchi, C.. IOS Press, 1st ed. www.florisbex.com/papers/COMMA2020IGN.pdf.
    Wilkinson, M. (2013): “Testing the Null Hypothesis: The Forgotten Legacy of Karl Popper?” Journal of Sports Sciences, Vol. 31, No. 9, pp. 919–920.
    Wooldridge, J. (2009): Should Instrumental Variables Be Used as Matching Variables? Technical Report, Michigan State University. www.msu.edu/~ec/faculty/wooldridge/current%20research/treat1r6.pdf.
    Zhang, K. and Hyvärinen, A. (2009): “On the Identifiability of the Post-Nonlinear Causal Model.” Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. https://arxiv.org/abs/1205.2599.
    Zhang, L. (2005): “The Value Premium.” Journal of Finance, Vol. 60, pp. 67–103.

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