☀️
Innovations in CoEfAs (e.g., GiveWell)
  • Cost-Effect-Analysis: Quant. uncertainty, transparent, customize
  • Organization and introduction
    • Using this resource
    • Key writings and resources
    • Who is involved?
    • Opportunities to contribute to this project
  • Innovations and issues
    • Limitations of GiveWell
      • (Possible errors and misunderstandings: examples from GW and beyond)
    • Incorporating uncertainty
    • (User-input, sensitivity checks)
    • (Type checking and code)
  • Tools and examples
    • Givewell models in explained maths
    • Squiggle
    • Causal.app
      • Causal/Givewell -- working examples (in progress)
    • Guesstimate
    • Pedant
    • hesim and other R package
    • cole_haus modeling
    • Other examples (MC/Fermi)
  • GiveWell model (and extensions)
    • Code representations of GW models
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  1. Tools and examples

Guesstimate

PreviousCausal/Givewell -- working examples (in progress)NextPedant

Last updated 3 years ago

Pros

  • Free and EA-aligned

  • Graphical, interesting 'multiple flows' display

  • Flexible, some good sensitivity analysis

  • Clearly shows 'dependencies'

  • Leaves out a lot of finance bells & whistles we don't need

  • Connected (?) to some other tech or coding stuff (not sure), Ozzie Goien, Squiggle (?), "Slurp?"

  • Nice documentation

Cons

  • Not code-based

  • No way to integrate with data (?)

  • Not as well-maintained or user-friendly as Causal

  • Object (box) labels hard to keep track of

  • No easy 'time series'

Below, I just added a few boxes in two rows,

  • some stochastic (random draws, normal, etc)

  • some determined (just numbers)

  • some computing a formula as a function of the others

  • then some 'sensitivity analysis' which I don't full understand

And an expanded view of the above:

here, in a gitbook
Reinstein messing around