References

Angrist, Joshua, and Guido Imbens. 1995. “Identification and Estimation of Local Average Treatment Effects.” National Bureau of Economic Research Cambridge, Mass., USA.
Carver, Robert, Michelle Everson, John Gabrosek, Nicholas Horton, Robin Lock, Megan Mocko, Allan Rossman, et al. 2016. “Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016.”
Cole, Stephen R, Robert W Platt, Enrique F Schisterman, Haitao Chu, Daniel Westreich, David Richardson, and Charles Poole. 2010. “Illustrating Bias Due to Conditioning on a Collider.” International Journal of Epidemiology 39 (2): 417–20.
Cozby, Paul C, and Scott Bates. 2020. Methods in Behavioral Research. McGraw-Hill Education.
Cummiskey, Kevin, Bryan Adams, James Pleuss, Dusty Turner, Nicholas Clark, and Krista Watts. 2020. “Causal Inference in Introductory Statistics Courses.” Journal of Statistics Education 28 (1): 2–8.
Dagan, Noa, Noam Barda, Eldad Kepten, Oren Miron, Shay Perchik, Mark A Katz, Miguel A Hernán, Marc Lipsitch, Ben Reis, and Ran D Balicer. 2021. “BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting.” New England Journal of Medicine.
Ellickson, Phyllis L, Joan S Tucker, and David J Klein. 2001. “High-Risk Behaviors Associated with Early Smoking: Results from a 5-Year Follow-up.” Journal of Adolescent Health 28 (6): 465–73.
Elwert, Felix, and Christopher Winship. 2014. “Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable.” Annual Review of Sociology 40: 31–53.
Greenland, Sander, and James M Robins. 1986. “Identifiability, Exchangeability, and Epidemiological Confounding.” International Journal of Epidemiology 15 (3): 413–19.
Hammond, E Cuyler, and Daniel Horn. 1954. “The Relationship Between Human Smoking Habits and Death Rates: A Follow-up Study of 187,766 Men.” Journal of the American Medical Association 155 (15): 1316–28.
Hammond, E Cuyler, and others. 1966. “Smoking in Relation to the Death Rates of One Million Men and Women.” Natl Cancer Inst Monogr 19 (166): 127–204.
Hernán, Miguel A, Sonia Hernández-Dı́az, and James M Robins. 2004. “A Structural Approach to Selection Bias.” Epidemiology, 615–25.
Hernán, Miguel A, Sonia Hernández-Dı́az, Martha M Werler, and Allen A Mitchell. 2002. “Causal Knowledge as a Prerequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology.” American Journal of Epidemiology 155 (2): 176–84.
Hernán, Miguel A, John Hsu, and Brian Healy. 2019. “A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks.” Chance 32 (1): 42–49.
Horton, Nicholas J. 2015. “Challenges and Opportunities for Statistics and Statistical Education: Looking Back, Looking Forward.” The American Statistician 69 (2): 138–45.
Kaplan, Daniel. 2018. “Teaching Stats for Data Science.” The American Statistician 72 (1): 89–96.
Lübke, Karsten, Matthias Gehrke, Jörg Horst, and Gero Szepannek. 2020. “Why We Should Teach Causal Inference: Examples in Linear Regression with Simulated Data.” Journal of Statistics Education 28 (2): 133–39.
Pearl, Judea. 1993. “[Bayesian Analysis in Expert Systems]: Comment: Graphical Models, Causality and Intervention.” Statistical Science 8 (3): 266–69.
Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect. Basic books.
Polack, Fernando P, Stephen J Thomas, Nicholas Kitchin, Judith Absalon, Alejandra Gurtman, Stephen Lockhart, John L Perez, et al. 2020. “Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine.” New England Journal of Medicine 383 (27): 2603–15.
Simantov, Elisabeth, Cathy Schoen, and Jonathan D Klein. 2000. “Health-Compromising Behaviors: Why Do Adolescents Smoke or Drink?: Identifying Underlying Risk and Protective Factors.” Archives of Pediatrics & Adolescent Medicine 154 (10): 1025–33.