Morgan, S., and Winship, C. (2015). Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research). 2nd ed. Cambridge University Press, New York, NY.
Neyman, J. (1923). On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statistical Science 5: 465–480.
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, NY.
Pearl, J. (2009). Causality: Models, Reasoning, and Inference. 2nd ed. Cambridge University Press, New York, NY.
Pearl, J., Glymour, M., and Jewell, N. (2016). Causal Inference in Statistics: A Primer. Wiley, New York, NY.
Reid, C. (1998). Neyman. Springer-Verlag, New York, NY.
Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66: 688–701.
Sekhon, J. (2007). The Neyman-Rubin model of causal inference and estimation via matching methods. In The Oxford Handbook of Political Methodology (J. M. Box-Steffensmeier, H. E. Brady, and D. Collier, eds.). Oxford University Press, Oxford, UK.
Shpitser, I., and Pearl, J. (2009). Effects of treatment on the treated: Identification and generalization. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press, Montreal, Quebec, 514–521.
Stott, P. A., Allen, M., Christidis, N., Dole, R. M., Hoerling, M., Huntingford, C., Pardeep Pall, J. P., and Stone, D. (2013). Attribution of weather and climate-related events. In Climate Science for Serving Society: Research, Modeling, and Prediction Priorities (G. R. Asrar and J. W. Hurrell, eds.). Springer, Dordrecht, Netherlands, 449–484.
Tian, J., and Pearl, J. (2000). Probabilities of causation: Bounds and identification. Annals of Mathematics and Artificial Intelligence 28: 287–313.
Trenberth, K. (2012). Framing the way to relate climate extremes to climate change. Climatic Change 115: 283–290.
VanderWeele, T. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press, New York, NY.
Глава 9. Опосредование: в поисках механизма действия
Annotated Bibliography
There are several books dedicated to the topic of mediation. The most up-to-date reference is VanderWeele (2015); MacKinnon (2008) also contains many examples. The dramatic transition from the statistical approach of Baron and Kenny (1986) to the counterfactual-based approach of causal mediation is described in Pearl (2014) and Kline (2015). McDonald’s quote (to discuss mediation, “start from scratch”) is taken from McDonald (2001).
Natural direct and indirect effects were conceptualized in Robins and Greenland (1992) and deemed problematic. They were later formalized and legitimized in Pearl (2001), leading to the Mediation Formula.
In addition to the comprehensive text of VanderWeele (2015), new results and applications of mediation analysis can be found in De Stavola et al. (2015); Imai, Keele, and Yamamoto (2010); and Muthén and Asparouhov (2015). Shpitser (2013) provides a general criterion for estimating arbitrary path-specific effects in graphs.
The Mediation Fallacy and the fallacy of “conditioning” on a mediator are demonstrated in Pearl (1998) and Cole and Hernán (2002). Fisher’s falling for this fallacy is told in Rubin (2005), whereas Rubin’s dismissal of mediation analysis as “deceptive” is expressed in Rubin (2004).
The startling story of how the cure for scurvy was “lost” is told in Lewis (1972) and Ceglowski (2010). Barbara Burks’s story is told in King, Montañez Ramírez, and Wertheimer (1996); the quotes from Terman and Burks’s mother are drawn from the letters (L. Terman to R. Tolman, 1943).
The source paper for the Berkeley admissions paradox is Bickel, Hammel, and O’Connell (1975), and the ensuing correspondence between him and Kruskal is found in Fairley and Mosteller (1977).
VanderWeele (2014) is the source for the “smoking gene” example, and Bierut and Cesarini (2015) tells the story of how the gene was discovered.
The surprising history of tourniquets, before and during the Gulf War, is told in Welling et al. (2012) and Kragh et al. (2013). The latter article is written in a personal and entertaining style that is quite unusual for a scholarly publication. Kragh et al. (2015) describes the research that unfortunately failed to prove that tourniquets improve the chances for survival.
References
Baron, R., and Kenny, D. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51: 1173–1182.
Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science 187: 398–404.
Bierut, L., and Cesarini, D. (2015). How genetic and other biological factors interact with smoking decisions. Big Data 3: 198–202.
Burks, B. S. (1926). On the inadequacy of the partial and multiple correlation technique (parts I–II). Journal of Experimental Psychology 17: 532–540, 625–630.
Burks, F., to Mrs. Terman. (June 16, 1943). Correspondence. Lewis M. Terman Archives, Stanford University.
Ceglowski, M. (2010). Scott and scurvy. Idle Words (blog). Available at: http://www.idlewords.com/2010/03/scott_and_scurvy.htm (posted: March 6, 2010).
Cole, S., and Hernán, M. (2002). Fallibility in estimating direct effects. International Journal of Epidemiology 31: 163–165.
De Stavola, B. L., Daniel, R. M., Ploubidis, G. B., and Micali, N. (2015). Mediation analysis with intermediate confounding. American Journal of Epidemiology 181: 64–80.
Fairley, W. B., and Mosteller, F. (1977). Statistics and Public Policy. Addison-Wesley, Reading, MA.
Imai, K., Keele, L., and Yamamoto, T. (2010). Identification, inference, and sensitivity analysis for causal mediation effects. Statistical Science 25: 51–71.
King, D. B., Montañez Ramírez, L., and Wertheimer, M. (1996). Barbara Stoddard Burks: Pioneer behavioral geneticist and humanitarian. In Portraits of Pioneers in Psychology (C. W. G. A. Kimble and M. Wertheimer, eds.), vol. 2. Erlbaum Associates, Hillsdale, NJ, 212–225.
Kline, R. B. (2015). The mediation myth. Chance 14: 202–213.
Kragh, J. F., Jr., Nam, J. J., Berry, K. A., Mase, V. J., Jr., Aden, J. K., III, Walters, T. J., Dubick, M. A., Baer, D. G., Wade, C. E., and Blackbourne, L. H. (2015). Transfusion for shock in U.S. military war casualties with and without tourniquet use. Annals of Emergency Medicine 65: 290–296.
Kragh, J. F., Jr., Walters, T. J., Westmoreland, T., Miller, R. M., Mabry, R. L., Kotwal, R. S., Ritter, B. A., Hodge, D. C., Greydanus, D. J., Cain, J. S., Parsons, D. S., Edgar, E. P., Harcke, T., Baer, D. G., Dubick, M. A., Blackbourne, L. H., Montgomery, H. R., Holcomb, J. B., and Butler, F. K. (2013). Tragedy into drama: An American history of tourniquet use in the current war. Journal of Special Operations Medicine 13: 5–25.
Lewis, H. (1972). Medical aspects of polar exploration: Sixtieth anniversary of Scott’s last expedition. Journal of the Royal Society of Medicine 65: 39–42.
MacKinnon, D. (2008). Introduction to Statistical Mediation Analysis. Lawrence Erlbaum Associates, New York, NY.
McDonald, R. (2001). Structural equations modeling. Journal of Consumer Psychology 10: 92–93.
Muthén, B., and Asparouhov, T. (2015). Causal effects in mediation modeling. Structural Equation Modeling 22: 12–23.
Pearl, J. (1998). Graphs, causality, and structural equation models. Sociological Methods and Research 27: 226–284.
Pearl, J. (2001). Direct and indirect effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco, CA, 411–420.
Pearl, J. (2014). Interpretation and identification of causal mediation. Psychological Methods 19: 459–481.
Robins, J., and Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology 3: 143–155.
Rubin, D. (2004). Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics 31: 161–170.
Rubin, D. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association 100: 322–331.
Shpitser, I. (2013). Counterfactual graphical models for longitudinal mediation analysis with unobserved confounding. Cognitive Science 37: 1011–1035.
Terman, L., to Tolman, R. (August 6, 1943). Correspondence. Lewis M. Terman Archives, Stanford University.
VanderWeele, T. (2014). A unification of mediation and interaction: A four-way decomposition. Epidemiology 25: 749–761.
VanderWeele, T. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. Oxford University Press, New York, NY.
Welling, D., MacKay, P., Rasmussen, T., and Rich, N. (2012). A brief history of the tourniquet. Journal of Vascular Surgery 55: 286–290.
Глава 10. Большие данные, искусственный интеллект и важные вопросы
Annotated Bibliography
An accessible source for the perpetual free will debate is Harris (2012). The compatibilist school of philosophers is represented in the writings of Mumford and Anjum (2014) and Dennett (2003).
Artificial intelligence conceptualizations of agency can be found in Russell and Norvig (2003) and Wooldridge (2009). Philosophical views on agency are compiled in Bratman (2007). An intent-based learning system is described in Forney et al. (2017).
The twenty-three principles for “beneficial AI” agreed to at the 2017 Asilomar meeting can be found at Future of Life Institute (2017).