Research

Topics and resources I am actively following.

• Causal inference — foundations
  • Judea Pearl — The Book of Why (2018)
  • Pearl — Introduction to Causal Inference (Statistics Surveys, 2009)
  • Pearl — Causality (2000)
• Transportability and data fusion
  • Bareinboim & Pearl — Causal Inference and the Data-Fusion Problem (PNAS, 2016)
  • Bareinboim's Causal AI Lab, Columbia
• Applied causal inference and modern treatments
  • Brady Neal — Introduction to Causal Inference (2020, free)
  • Hernán & Robins — Causal Inference: What If (2020, free PDF)
  • Facure — Causal Inference for the Brave and True (free)
  • Peters, Janzing & Schölkopf — Elements of Causal Inference (2017, free PDF)
• Decision calibration and forecasting
  • Tetlock & Gardner — Superforecasting (2015)
  • Good Judgment Project
• Cognitive biases and decision architecture
  • Kahneman — Thinking, Fast and Slow (2011)
  • Kahneman, Sibony & Sunstein — Noise (2021)
• Naturalistic decision-making
  • Gary Klein — Sources of Power (1998)
• Computational decision-making and metacognition
  • Gershman Lab, Harvard — computational cognitive science
  • NYU Center for Decision Sciences
• Ergodicity and non-ergodic decision-making
  • Ole Peters — The ergodicity problem in economics (Nature Physics, 2019)
  • LML Ergodicity Economics
• Robust optimization and fat tails
  • Ben-Tal, El Ghaoui & Nemirovski — Robust Optimization
  • Taleb — Statistical Consequences of Fat Tails
• Bayesian reasoning
  • E.T. Jaynes — Probability Theory: The Logic of Science
• Ongoing signal
  • Ferenc Huszár — inference.vc
  • Andrew Gelman — Statistical Modeling blog