The Reproducibility Working Group
Ford Hall, Room 300
The Reproducibility Working Group is an interdisciplinary collaboration among faculty, post-docs, and graduate students in philosophy, psychology, statistics, and other areas that coalesced at the end of the spring semester after biweekly discussions on questions about reproducibility in psychology hosted by the Minnesota Center for Philosophy of Science. Last semester (Fall 2018), we explored questions about measurement in the context of psychometrics by reading Measuring the Mind: Conceptual Issues in Contemporary Psychometrics (Cambridge University Press, 2005) every other week over lunch together. We finished the semester with a visit and two lectures from Joel Michell (University of Sydney) on the logic of measurement and psychometrics.
This semester we are continuing these discussions with a biweekly lunch discussion on Tuesdays (11:45am-1pm) to study another of these issues in more depth: causal inference. We will be reading a variety of articles offering different perspectives on causal inference, methodology, and reproducibility. The schedule of meetings and readings (to be held in 300 Ford Hall) is:
- Hill, A.B. 1965. The environment of disease: association or causation? Proceedings of the Royal Society of Medicine 58:295–300. [reprinted in 2005]
- Munafò, M.R. and G. Davey. 2018. Repeating experiments is not enough. Nature 553:399-401
- Pearl, J. 2018. Does obesity shorten life? Or is it the soda? On non-manipulable causes. Journal of Causal Inference 6(2):2001
- Greenland, S. and J. Pearl. 2017. Causal diagrams. Wiley StatsRef: Statistics Reference Online. John Wiley & Sons, Ltd. DOI: 10.1002/9781118445112.stat03732.pub2
- Rubin, D.B. 2005. Causal inference using potential outcomes. Journal of the American Statistical Association 100:322–331
- Stegenga, J. 2009. Robustness, discordance, and relevance. Philosophy of Science 76:650–661
- Benson, K. and A.J. Hartz. 2000. A comparison of observational studies and randomized, controlled trials. The New England Journal of Medicine 342:1878–1886.
- Hernán, M.A. 2016. Does water kill? A call for less casual causal inferences. Annals of Epidemiology 26:674–680.
Kuorikoski, J. and C. Marchionni. 2016. Evidential diversity and the triangulation of phenomena. Philosophy of Science 83:227–247.
- Greenland, S. 2017. For and against methodologies: some perspectives on recent causal and statistical inference debates. European Journal of Epidemiology 32:3-20.
- Kaufman, J.S. 2019. Commentary: causal inference for social exposures. Annual Review of Public Health. doi:10.1146/annurev-publhealth-040218-043735
- Vandenbroucke, J.P., A. Broadbent, and N. Pearce. 2016. Causality and causal inference in epidemiology: the need for a pluralistic approach. International Journal of Epidemiology 45:1776-1786.
- No new readings for this session. We will continue discussion of themes in the readings from 3/12 (Greenland 2017; Kaufman 2019; Vandenbroucke et al. 2016), as well as Hernán 2016 (from 2/26).
- Mayo-Wilson, C. 2018. Causal identifiability and piecemeal experimentation. Synthese. https://doi.org/10.1007/s11229-018-1826-4
- Ramsey, J.D., K. Zhang, and C. Glymour. 2019. The evaluation of discovery: models, simulation and search through “Big Data”. Open Philosophy 2: 39–48. https://doi.org/10.1515/opphil-2019-0005
- Fletcher, S,C,, J. Landes, and R. Poellinger. 2018. Evidence amalgamation in the sciences: an introduction. Synthese. https://doi:10.1007/s11229-018-1840-6
- Patrick Suppes on Data Models (1960)
- Cox, L.A. 2018. Modernizing the Bradford Hill criteria for assessing causal relationships in observational data. Critical Reviews in Toxicology 48:682–712.
- Glass, T.A., S.N. Goodman, M.A. Hernán, and J.M. Samet. 2013. Causal inference in public health. Annual Review of Public Health 34:61–75.
- Goldman, G.T. and F. Dominici. 2019. Don’t abandon evidence and process on air pollution policy. Science 363:1398–1400. Supplementary Material
5/2-5/3: Causal Inference and Data Science, Institute for Research in Statistics and its Applications Annual Conference
- Readings are accessible to all University of Minnesota community members (click on the links above). For any questions about access, please contact Amanda Schwartz (firstname.lastname@example.org).
Note: lunch will be provided for participants. New participants please RSVP to Amanda Schwartz (email@example.com) no later than the Thursday preceding the RGW meeting (e.g., Thursday 1/24 for session on 1/29). Participants are welcome to join for any and all sessions as their schedules permit.
The Reproducibility Working Group is jointly sponsored by the Commons for Research in the Social Sciences, the Institute for Research in Statistics and its Applications, and the Minnesota Center for Philosophy of Science.