Paper selection

For the paper presentations in course week 8, you need to do the following:

• Choose a research paper on causality & causal inference, alone or together with a partner.
• You will present the main points of the paper in 10-15 minutes during the last week of the course. 
  • Your goal is to communicate the main point of the paper using concepts we have developed in this course
  • Focus on communicating a) which problem is being solved, b) which solution was chosen, and c) whether the goal of the paper was met. 
• Papers will be presented in groups of 1 or 2 students
• Submit this assignment with a text entry indicating your selected paper (if you work in a group, you should both submit this assignment—name who you will be working with)

Below are a list of example papers, but you are welcome to choose other papers as well. 

• Single World Intervention Graphs: A Primer, Richardson & Robins, 2013
  http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.644.1881&rep=rep1&type=pdf 
• Sensitivity to Hidden Bias, Chapter 4 of Rosenbaum, Observational studies, 2002
  https://link.springer.com/chapter/10.1007/978-1-4757-3692-2_4 
• Quantifying Causal Influences, Janzing et al., 2014
  https://arxiv.org/pdf/1203.6502.pdf 
• Does obesity shorten life? The importance of well-defined interventions to answer causal questions, Hernán & Taubman, 2008
  https://www.nature.com/articles/ijo200882 
• Identification of causal effects using instrumental variables, Angrist, Imbens & Rubin, 1996
  https://www.tandfonline.com/doi/pdf/10.1080/01621459.1996.10476902 
• The central role of the propensity score in observational studies for causal effects, Rosenbaum & Rubin, 1983
  https://academic.oup.com/biomet/article/70/1/41/240879 
• Optimal Matching for Observational Studies, Rosenbaum, 1989
  https://amstat.tandfonline.com/doi/abs/10.1080/01621459.1989.10478868  
• The seven tools of causal inference, with reflections on machine learning, Pearl, 2019
  https://dl.acm.org/doi/pdf/10.1145/3241036 
• Avoiding Discrimination through Causal Reasoning, Kilbertus et al., 2017
  http://papers.nips.cc/paper/6668-avoiding-discrimination-through-causal-reasoning.pdf 
• Nonlinear causal discovery with additive noise models, Hoyer et al., 2009
  https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models.pdf 
• Identifiability of path-specific effects, Avin, Shpitser & Pearl, 2005
  https://escholarship.org/content/qt45x689gq/qt45x689gq.pdf 
• On Causal and Anticausal Learning, Schölkopf et al., 2012
  https://icml.cc/2012/papers/625.pdf 
• Marginal Structural Models and Causal Inference in Epidemiology
  https://www.jstor.org/stable/pdf/3703997.pdf 
• Instruments for causal inference: an epidemiologist's dream?, Hernán & Robins, 2006
  https://www.jstor.org/stable/pdf/20486236.pdf 
• Optimal dynamic treatment regimes, Murphy, 2003
  https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/1467-9868.00389